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Handwriting typically consists of a wide range of writing forms with substantial differences in the placements and size of those writing shapes. The arrangement, organization, and spatial association of individual letters, words, and phrases on a written page are all examples of handwritten text structure. This study proposed a novel approach in which a handwritten text structure is primarily transformed into a Cartesian XY-coordinate system while maintaining its shape characteristics. The points on the contour of handwritten text with local maximum bending values are considered hypothetical breakdown points called Key Points (KPs). From the skeletal representation of handwriting, this approach first identifies KPs and their coordinate positions. After that, KPs are transformed into a graph-based representation with vertices and edges. With the use of geometric graphs, this representation attempts to capture the spatial and temporal organization of handwriting. Previous techniques focused on statistical methods, which offer fixed-size descriptions; however, graph-based representations are flexible in size and reveal the relationship between text structure. Graph representations enable the incorporation of contextual information by integrating extra features which improve text accuracy and understanding. The proposed approach is tested for writer identification and verification tasks on four benchmark datasets (CERUG-EN, CVL, Firemaker, and IAM) and one custom-built dataset. The findings demonstrated that the proposed approach obtained state-of-the-art accuracies across all the mentioned datasets. Particularly, it achieved the highest accuracy of 99.75% for identification and 99.81% for verification on the Firemaker dataset. [ABSTRACT FROM AUTHOR]

Writer identification form historical manuscripts presents a challenging problem with significant implications for understanding the authorship of ancient texts. In this paper, we propose a novel deep learning framework tailored for the task of historical manuscripts writer identification. Our approach leverages data-driven features, harnessing the power of neural networks to extract and learn discriminative patterns from handwritten historical documents. The key innovation of our framework lies in its ability to automatically discover and utilize relevant features from data to profile the writer, eliminating the need for manual feature engineering. Our methodology encompasses three well-defined steps: initially, manuscript preprocessing involves image denoising using advanced techniques such as non-local means and total-variation, followed by binarization using a Canny-edge detector. In the subsequent phase, we employ Harris corner detector for automatic key-point detection and clustering, allowing us to identify the regions of interest within the documents. Lastly, the features extracted from these regions are subjected to classification through transfer learning, utilizing a deep learning-based model specifically trained on the extracted patches. To achieve the final document-level identification, we enhance the system accuracy by implementing a majority vote scheme, where the aggregated decisions from multiple patches contribute to the ultimate classification outcome. We validate our approach on "ICDAR 2017" dataset, spanning different periods and writing styles of historical manuscripts. Experimental results demonstrate the superior performance of our method in accurately identifying the authors of historical documents, surpassing existing techniques. Moreover, our framework exhibits robustness in scenarios where limited training data is available. This work not only contributes to the field of historical manuscripts analysis but also highlights the potential of deep learning in solving intricate problems in the realm of document analysis and authorship attribution. Our framework offers a promising avenue for scholars and historians to gain deeper insights into the authors of historical texts, opening new doors for historical research and preservation. [ABSTRACT FROM AUTHOR]

Since the dawn of civilization, handwriting has been one of the most important forms of communication. However, as handwriting differs from person to person, writer identification has become a promising application of pattern recognition to identify the actual writer of a handwritten document. Handwriting can be either online or offline, depending on how it was obtained. Users can write directly on tablets, smartphones, touch screens, PDAs, and other devices using input devices with online handwriting, whereas offline handwriting is done with a pen and paper. With the advent of artificial intelligence, and most importantly deep learning techniques, the development of writer identification systems based on offline handwritten documents has gained a lot of attention. Deep learning models have the capability of automatic feature extraction, which results in increased performance. From the literature survey, it was revealed that least attention has been paid towards the development of deep learning-based writer identification systems for offline Urdu handwritten documents, unlike the English and Arabic scripts. Therefore, in this paper, we proposed an offline Urdu handwritten writer identification system using a deep learning model inspired by the VGG-16 model of CNN. The model was trained and tested on a novel Urdu handwritten dataset contributed by 318 distinct Urdu writers, resulting in an overall training accuracy of 98.71% and a testing accuracy of 99.11%. The results achieved showed that the proposed model outperformed the already existing writer identification techniques. [ABSTRACT FROM AUTHOR]

The extraction of paleographical features is an important task to study the identity of the text in the Historical Manuscripts. One of the major features is the identification of the writer or copyist. Many researchers have worked on an automated system for writer identification, and with the development of deep learning techniques many approaches have been proposed. Most of the previous studies have developed a multi-steps system, while very few of them performed an End-to-End approach. Most of the systems rely on a pre-processing step to prepare the data in order to facilitate recognition. This paper presents an End-to-End deep learning system for writer identification, tested on four different datasets: ICDAR19 and ICFHR20 (Latin datasets), KHATT and Balamand (Arabic datasets). The system is based on the Deep-TEN approach using a customized ResNet-50 network for features and local descriptor extraction with an integration of a NetVLAD end-layer to compute and encode the global descriptor. It was compared with our state-of-the-art system, winner of ICFHR20 HisFrag competition, and showed an interesting performance on all datasets without any pre-processing techniques. [ABSTRACT FROM AUTHOR]

In this work, we present two advanced models for identifying script writers, leveraging the power of deep learning. The proposed systems utilize the new vision Swin Transformer and ResNeSt-50. Swin Transformer is known for its robustness to variations and ability to model long-range dependencies, which helps capture context and make robust predictions. Through extensive training on large datasets of handwritten text samples, the Swin Transformer operates on sequences of image patches and learns to establish a robust representation of each writer’s unique style. On the other hand, ResNeSt-50 (Residual Neural Network with Squeeze-and-Excitation (SE) and Next Stage modules), with its multiple layers, helps in learning complex representations of a writer’s unique style and distinguishing between different writing styles with high precision. The SE module within ResNeSt helps the model focus on distinctive handwriting characteristics and reduce noise. The experimental results demonstrate exceptional performance, achieving an accuracy of 98.50% (at patch level) by the Swin Transformer on the CVL database, which consists of images with cursively handwritten German and English texts, and an accuracy of 96.61% (at page level) by ResNeSt-50 on the same database. This research advances writer identification by showcasing the effectiveness of the Swin Transformer and ResNeSt-50. The achieved accuracy underscores the potential of these models to process and understand complex handwriting effectively.

In recent years, there has been a significant increase in document forgery, which includes the fraudulent replication of currency, diplomas, and works of art. This has become a major issue due to the widespread usage of paper-based documentation. Handwriting is closely linked to document forgery and forensics as it possesses unique characteristics, including variations in text characters, pen pressure, writing angle, and stroke patterns, which makes it impossible to replicate accurately. As a result, handwriting serves as a personalized biometric that can be used to determine the authenticity of documents. However, traditional methods of writer identification are both time-consuming and destructive, requiring substantial expertise. To overcome these limitations, the study explores the potential of hyperspectral imaging (HSI) as a non-destructive and advanced approach for detecting and preventing document forgery. HSI provides detailed spectral information from a scene, making it possible to capture subtle spectral differences in handwriting samples. This imaging technique has diverse applications in various fields such as agriculture, environmental monitoring, remote sensing, forensics, document analysis, and medical imaging. Our study proposes a novel unsupervised approach, CAE-SVM that uses Convolutional Autoencoder (CAE) for feature extraction and Support Vector Machine (SVM) for writer identification. It was tested on the UWA writing ink hyperspectral images dataset for blue and black inks which is available publicly and compared with state-of-the-art methods and CNN. The proposed approach achieved the highest accuracy of 92.78% for blue ink, surpassing existing methods. The study's results emphasize the efficacy of HSI as a potent forensic analysis tool for detecting and preventing document forgery. [ABSTRACT FROM AUTHOR]

Handwriting is an individual trait that serves as evidence to authenticate a particular writer. Identifying the writer of a handwritten text has shown encouraging results in examining historical and forensic documents. In this paper, we propose a novel offline writer identification system based on the challenging analysis of small amount of data to extract distinct patterns. In our deep network, the feature extraction process relies on a specially designed dual-path architecture, and the resulting embeddings are concatenated to produce the final learned features. To deal with a variety of uncertainties such as high intra-class variations and noises, we leverage the fuzzy logic in the design of a custom Convolutional Neural Network (CNN) with a type-2 fuzzy activation function for the first path. Additionally, the second path utilizes the transfer learning-based CNN to enhance the discriminability of the learned features. Our method allows for text-independent writer identification, eliminating the need for identical handwriting samples to train and test the model. Considering that various factors can influence the handwriting style, a dataset containing right-to-left handwriting samples is assembled. The proposed method is evaluated on our developed dataset and four widely-known public datasets, namely KHATT, CVL, Firemaker, and IAM. High accuracy values are achieved, with results of 99.85%, 99.83%, 99.79%, 99.64%, and 98.17% for each dataset, respectively. One noteworthy aspect of this study is that the evaluation results on diverse datasets demonstrate the applicability of the proposed model to various languages. Moreover, the model performs effectively in real-world scenarios with limited handwritten data. [ABSTRACT FROM AUTHOR]

The writer identification task infers the writer by analyzing the texture, structure, and other representative features of the handwriting. Inspired by the attention mechanism, an end-to-end writer identification model is proposed in this paper, which combines both global features and local features. The Vision Transformer is used as the backbone network, and the Convolutional block attention module (CBAM) is introduced to enhance the ability of global feature awareness of the model. The proposed method is evaluated on two public data sets, IAM and CVL respectively. In the task of word-level writer identification, the accuracy rates in two data sets were 90.1% and 92.3% respectively. In the task of page-level writer identification, the accuracy rates were 98.6% and 99.5%, as a state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Signature is used as an important, typical means of identification in banking, security controls, certificates, and contracts. Moreover, it has an intensive role in legal issues that need to be studied, considering the development of related applications. From this standpoint, this study proposes a new method for identifying offline signatures under different uncertainties such as various experimental conditions and environmental noises using deep learning approaches. To achieve this objective, a comprehensive right-to-left signature dataset based on relevant standards is collected from 85 participants at various time intervals under different experimental conditions. A developed deep neural network based on transfer learning networks is designed to extract features from raw data in a hierarchical manner. One of the benefits of the proposed method is that it is independent of right-handed or left-handed people and can be applied to both. The method proposed is examined not just on the collected dataset but also on a variety of other datasets. The proposed network has a 99% accuracy for author signature classification and can withstand a wide range of SNRs. As a result, the classification accuracy at 15 dB remains above 90%. The study's findings show that the proposed network can learn features hierarchically from raw signature data and achieve greater accuracy than other methods. Because of its superior performance, the proposed model can be used as an assistant by signature experts in a variety of applications, including the detection of forgery and criminals. [ABSTRACT FROM AUTHOR]

Abstract The main goal of the present work is to determine the hand that has written two newly discovered documents in Romania. For giving the proper answer, the authors introduced the notion of “Ideal Representative”, namely of an object that very well represents the corresponding ideal alphabet symbol that a writer had in his/her mind when writing a document by hand. Moreover, the authors have introduced a novel method, which leads to the optimal evaluation of the Ideal Representative of any alphabet symbol in association with any handwritten document. Furthermore, the authors have introduced methods for comparing these Ideal Representatives, so as a final decision about the hand that has written a document may be obtained with a highly considerable likelihood. The related analysis manifests that the two documents discovered in Romania in 1998, belong to the great personality of Rigas Feraios. The presented method of automatic handwriting Identification seems to be of general applicability.

Identifying the writer of a handwritten document has remained an interesting pattern classification problem for document examiners, forensic experts, and paleographers. While mature identification systems have been developed for handwriting in contemporary documents, the problem remains challenging from the viewpoint of historical manuscripts. Design and development of expert systems that can identify the writer of a questioned manuscript or retrieve samples belonging to a given writer can greatly help the paleographers in their practices. In this context, the current study exploits the textural information in handwriting to characterize writer from historical documents. More specifically, we employ oBIF(oriented Basic Image Features) and hinge features and introduce a novel moment-based matching method to compare the feature vectors extracted from writing samples. Classification is based on minimization of a similarity criterion using the proposed moment distance. A comprehensive series of experiments using the International Conference on Document Analysis and Recognition 2017 historical writer identification dataset reported promising results and validated the ideas put forward in this study.

• We consider large-scale supervised pre-training on a carefully designed synthetic dataset of word images. • We obtain robust writer's style representations, independent of the semantic content of the image. • We leverage the obtained representations for handwriting analysis tasks on real images from benchmark datasets. • Experiments demonstrate the suitability of our approach and its competitiveness compared to task-specific state-of-the-art. In this work, we explore massive pre-training on synthetic word images for enhancing the performance on four benchmark downstream handwriting analysis tasks. To this end, we build a large synthetic dataset of word images rendered in several handwriting fonts, which offers a complete supervision signal. We use it to train a simple convolutional neural network (ConvNet) with a fully supervised objective. The vector representations of the images obtained from the pre-trained ConvNet can then be considered as encodings of the handwriting style. We exploit such representations for Writer Retrieval, Writer Identification, Writer Verification, and Writer Classification and demonstrate that our pre-training strategy allows extracting rich representations of the writers' style that enable the aforementioned tasks with competitive results with respect to task-specific State-of-the-Art approaches. [ABSTRACT FROM AUTHOR]

The writer identification system identifies individuals based on their handwriting is a frequent topic in biometric authentication and verification systems. Due to its importance, numerous studies have been conducted in various languages. Researchers have established several learning methods for writer identification including supervised and unsupervised learning. However, supervised methods require a large amount of annotation data, which is impossible in most scenarios. On the other hand, unsupervised writer identification methods may be limited and dependent on feature extraction that cannot provide the proper objectives to the architecture and be misinterpreted. This paper introduces an unsupervised writer identification system that analyzes the data and recognizes the writer based on the inter-feature relations of the data to resolve the uncertainty of the features. A pairwise architecturebased Autoembedder was applied to generate clusterable embeddings for handwritten text images. Furthermore, the trained baseline architecture generates the embedding of the data image, and the K-means algorithm is used to distinguish the embedding of individual writers. The proposed model utilized the IAM dataset for the experiment as it is inconsistent with contributions from the authors but is easily accessible for writer identification tasks. In addition, traditional evaluation metrics are used in the proposed model. Finally, the proposed model is compared with a few unsupervised models, and it outperformed the state-of-the-art deep convolutional architectures in recognizing writers based on unlabeled data. [ABSTRACT FROM AUTHOR]

As an indispensable part of identity authentication, offline writer identification plays a notable role in biology, forensics, and historical document analysis. However, identifying handwriting efficiently, stably, and quickly is still challenging due to the method of extracting and processing handwriting features. In this paper, we propose an efficient system to identify writers through handwritten images, which integrates local and global features from similar handwritten images. The local features are modeled by effective aggregate processing, and global features are extracted through transfer learning. Specifically, the proposed system employs a pre-trained Residual Network to mine the relationship between large image sets and specific handwritten images, while the vector of locally aggregated descriptors with double power normalization is employed in aggregating local and global features. Moreover, handwritten image segmentation, preprocessing, enhancement, optimization of neural network architecture, and normalization for local and global features are exploited, significantly improving system performance. The proposed system is evaluated on Computer Vision Lab (CVL) datasets and the International Conference on Document Analysis and Recognition (ICDAR) 2013 datasets. The results show that it represents good generalizability and achieves state-of-the-art performance. Furthermore, the system performs better when training complete handwriting patches with the normalization method. The experimental result indicates that it’s significant to segment handwriting reasonably while dealing with handwriting overlap, which reduces visual burstiness. [ABSTRACT FROM AUTHOR]

Offline handwriting has become a very important part in the field of behavioral biometrics. In this paper, we proposed an approach to identify writers from their handwritten documents. The foremost contribution of this study is to suggest the use of co-occurrence features to improve the performance of writer identification. A contour texture-based feature is extracted from preprocessed regions of interest (components or sub-images) and their exterior contours are based primarily on Modified Local Binary Pattern (MLBP) and Ink-trace Width and Shape Letters (IWSL) measurements. Considering the contour as a texture, and using these textural descriptors, the joint probability distributions of MLBP and IWSL on different pixels are calculated in order to determine the similarities between different images of handwriting. Identification is carried out using the nearest neighbor rule and Chi-square distance. The proposed system has been evaluated on eight well-known handwriting databases (Arabic IFN/ENIT and KHATT, English IAM and CVL, Dutch Firemaker, Portuguese BFL, Chinese CERUG-CN, and English/Greek ICDAR2013). Experimental results show that the recommended scheme achieves the highest performance on KHATT, CVL, Firemaker, BFL, CERUG-CN, and ICDAR2013 databases, and that it demonstrates competitive performance on IFN/ENIT and IAM databases as compared to those reported by the state-of-the-art identification systems.

The main goal of the present work is to determine the hand that has written two newly discovered documents in Romania. For giving the proper answer, the authors introduced the notion of "Ideal Representative", namely of an object that very well represents the corresponding ideal alphabet symbol that a writer had in his/her mind when writing a document by hand. Moreover, the authors have introduced a novel method, which leads to the optimal evaluation of the Ideal Representative of any alphabet symbol in association with any handwritten document. Furthermore, the authors have introduced methods for comparing these Ideal Representatives, so as a final decision about the hand that has written a document may be obtained with a highly considerable likelihood. The related analysis manifests that the two documents discovered in Romania in 1998, belong to the great personality of Rigas Feraios. The presented method of automatic handwriting Identification seems to be of general applicability. [ABSTRACT FROM AUTHOR]

Writer identification has steadily progressed in recent decades owing to its widespread application. Scenarios with extensive handwriting data such as page-level or sentence-level have achieved satisfactory accuracy; however, word-level offline writer identification is still challenging owing to the difficulty of learning good feature representations with scant handwriting data. This paper proposes a new Residual Swin Transformer Classifier (RSTC), which comprehensively aggregates local and global handwriting styles and yields robust feature representations with single-word images. The local information is modeled by the Transformer Block through interacting strokes and global information is featurized by holistic encoding using the Identity Branch and Global Block. Moreover, the pre-training technique is exploited to transfer reusable knowledge learned from a task similar to writer identification, strengthening RSTC’s representation of handwriting features. The proposed method is tested on the IAM and CVL benchmark datasets and achieves state-of-the-art performance, which demonstrates the superior modeling capability of RSTC for word-level writer identification.

Writer identification has become a hot research topic in the fields of pattern recognition, forensic document analysis, the criminal justice system, etc. The goal of this research is to propose an efficient approach for writer identification based on online handwritten Kanji characters. We collected 47,520 samples from 33 people who wrote 72 online handwritten-based Kanji characters 20 times. We extracted features from the handwriting data and proposed a support vector machine (SVM)-based classifier for writer identification. We also conducted experiments to see how the accuracy changes with feature selection and parameter tuning. Both text-dependent and text-independent writer identification were studied in this work. In the case of text-dependent writer identification, we obtained the accuracy of each Kanji character separately. We then studied the text-independent case by considering some of the top discriminative characters from the text-dependent case. Finally, another text-dependent experiment was performed by taking two, three, and four Kanji characters instead of using only one character. The experimental results illustrated that SVM provided the highest identification accuracy of 99.0% for the text-independent case and 99.6% for text-dependent writer identification. We hope that this study will be helpful for writer identification using online handwritten Kanji characters. [ABSTRACT FROM AUTHOR]

Offline handwriting has become a very important part in the field of behavioral biometrics. In this paper, we proposed an approach to identify writers from their handwritten documents. The foremost contribution of this study is to suggest the use of co-occurrence features to improve the performance of writer identification. A contour texture-based feature is extracted from preprocessed regions of interest (components or sub-images) and their exterior contours are based primarily on Modified Local Binary Pattern (MLBP) and Ink-trace Width and Shape Letters (IWSL) measurements. Considering the contour as a texture, and using these textural descriptors, the joint probability distributions of MLBP and IWSL on different pixels are calculated in order to determine the similarities between different images of handwriting. Identification is carried out using the nearest neighbor rule and Chi-square distance. The proposed system has been evaluated on eight well-known handwriting databases (Arabic IFN/ENIT and KHATT, English IAM and CVL, Dutch Firemaker, Portuguese BFL, Chinese CERUG-CN, and English/Greek ICDAR2013). Experimental results show that the recommended scheme achieves the highest performance on KHATT, CVL, Firemaker, BFL, CERUG-CN, and ICDAR2013 databases, and that it demonstrates competitive performance on IFN/ENIT and IAM databases as compared to those reported by the state-of-the-art identification systems. [ABSTRACT FROM AUTHOR]

Determining the writer or transcriber of historical Arabic manuscripts has always been a major challenge for researchers in the field of humanities. With the development of advanced techniques in pattern recognition and machine learning, these technologies have been applied to automate the extraction of paleographical features in order to solve this issue. This paper presents a baseline system for writer identification, tested on a Historical Arabic dataset of 11610 single and double folio images. These texts were extracted from a unique collection of 567 Historical Arabic Manuscripts available at the Balamand Digital Humanities Center. A survey has been conducted on the available Arabic datasets and previously proposed techniques and algorithms. The Balamand dataset presents an important challenge due to the geo-historical identity of manuscripts and their physical conditions. An advanced Deep Learning system was developed and tested on three different Latin and Arabic datasets: ICDAR19, ICFHR20 and KHATT, before testing it on the Balamand dataset. The system was compared with many other systems and it has yielded a state-of-the-art performance on the new challenging images with 95.2% mean Average Precision (mAP) and 98.1% accuracy. [ABSTRACT FROM AUTHOR]

Writer identification is an active research problem due to its applications in forensic and historic documents analysis. It is challenging to identify a writer from her handwritten characters' shapes produced via practiced writing style. Different writing shapes, styles, orientations, various sizes of characters, complex structures, inconsistency, and cursive nature of the text make it a tougher undertaking. To solve this problem, we need to explore a structural representation and spatial information of the handwritten characters. For this, a novel graph-based approach is proposed here to spatially map the handwritten text, adapt its structure, size, and explore the relationship that exist between them. First, image processing steps such as binarization, baseline correction, separation of the writing region, and thinning of the strokes to a width of a single pixel are executed. This work presents a novel algorithm for detecting key points (KPs) in a handwritten skeleton image and extracting their two-dimensional pixel coordinates values. The handwriting samples are then transformed into a graph-based representation with KPs representing nodes and the line segments connecting adjacent KPs as the edges. Features are extracted from the graph-based representations of the handwritten text. For classification, ensemble learning approaches are employed. Four benchmark datasets and one custom collected dataset are utilized for experimentations. The proposed solution achieves identification accuracies of 98.26%, 98.84%, 99.67%, 98.51%, and 97.73%, on CERUG-EN, CVL, Firemaker, IAM, and custom datasets, respectively. [ABSTRACT FROM AUTHOR]

Handwritten text recognition has been an ongoing attractive task to research in the field of document analysis and recognition with applications in handwriting forensics, paleography, document examination, and handwriting recognition. In the present research, an automatic method of writer recognition is presented using digitized images of unconstrained texts. Despite the increasing efforts by prior literature on the different methods used for the same purpose, such methods performance, particularly their accuracy, has not been promising, leaving plenty of room for improvements. This method made use of codebook-based writer characterization, with each writing sample represented by a group of computed features from a primary and secondary codebook. The writings were then represented through the computation of the probability of codebook patterns occurrence, and the probability distribution was employed for each writer’s characterization. Writer identification process involved comparing two writings through the computation of the distances between their respective probability distribution. The study carried out experiments to determine the performance of the implemented method in light of rates of identification with the help of standard datasets, namely, KRDOH and IAM, the former being the most current and largest Kurdish handwritten datasets with 1076 writers, and the latter being a dataset containing 650 writers. The outcome of the experiments was promising with a rate of identification of 94.3%, with the proposed method outperforming the state-of-the-art methods by 2–3%.

Writer identification is the procedure of identifying individuals from handwriting. Writer identification is a common interest in biometric authentication and verification systems, and numerous studies are available for English, Chinese, Arabic, and Persian specific handwriting. This paper introduces a supervised offline Indic script writer identification system that can identify individuals using less than a single page of handwriting. A lightweight Convolutional Neural Network (CNN) architecture fused with non-trainable Gabor filters is used as an identification model that can recognize writers from scarce data. For the experiment, we used BanglaWriting dataset, which is openly available for Bengali writing and writer recognition. Further, we added Devanagari and Telugu datasets for evaluation. The overall evaluation shows that the proposed thresholded Gabor-based CNN architecture performs superior to numerous deep CNN architectures for Indic writer recognition.

Recent advances in information technology and the need for more security have led to the rapid development of intelligent biometric identification systems. Recent studies have proven that handwriting of each person is unique and can be used as one of the authentication methods. There are many researches in the literature for writer identification on a specific language. Unfortunately, there are no necessary data sets for this purpose. In this paper, for the first time, a handwritten data set of 300 persons in both Persian and English languages was collected. The main goal of this paper is to provide a model to identify the writer independent of the language written in Persian and English. After pre-processing stage, each person's handwriting is converted into blocks of a certain size called a texture. Then, using these textures, the desired features are extracted. In order to extract these features, first a two-dimensional wavelet transform is applied to each image and then, using the new algorithm for calculating the fractal dimension, which is used for the first time in this field, the feature vector is obtained. Finally, MLP neural networks are utilized for classification step. The performance of the proposed method is evaluated in different scenarios.

Writer identification is a sub-field in handwriting recognition which its objective is to determine the identity of the writer based on handwriting input. The goal is usually for forensic purposes such as finding the perpetrators of crimes that leave traces of evidence in the form of written messages. In addition, writer identification can also be used to determine the identity of a historical actor if he or she leaves a valuable written artefact. The object of this research is the traditional character of the Lampung region which is so-called Had Lampung by the local community. The traditional character of Lampung consists of 20 main characters and 12 diacritics. Based on selected characters, the writer will be recognized using the Principal Component Analysis (PCA) feature. PCA is one linear feature extraction method of an object in pattern recognition. The PCA algorithm consists of several stages, namely the calculation of the average dataset, the subtraction of the vector dataset with averages, the calculation of covariance, the calculation of eigenvectors and eigenvalues, eigenvector reduction, and the projection of the dataset against reduced eigenvector space. PCA in this paper is used as a feature in image recognition. The dataset utilized in this study is the Lampung Dataset which is a handwritten character recognition (HWCR) dataset. Lampung Dataset consists of 82 Lampung handwritten documents. All Lampung character images in the dataset were extracted from these documents using the connected component extraction algorithm and eventually generated 32,140 images. Furthermore, these images are converted into grayscale images. In this research, as many as 12,500 grayscale images of Lampung handwriting characters were chosen to represent 82 different writers. This data is employed as training and testing data on the proposed method. The highest accuracy of the identification of the writer using this PCA feature is 82.92%, while the lowest accuracy is 28.29%.

This article presents a dataset of hyperspectral images of handwriting samples collected from 54 individuals. The purpose of the presented dataset is to further explore the use of hyperspectral imaging in document image analysis and to benchmark the performance of forensic analysis methods for hyperspectral document images. Each hyperspectral cube in the dataset has a spatial resolution of 512 × 650 pixels and contains 149 spectral channels in the spectral range of 478–901 nm. All the individuals have different personalities and have their writing patterns. The information of age and gender of each individual is collected. Each subject has written twenty-eight sentences using 12 different varieties of pens from different brands in blue color, each approximately 9 words or 33 characters long, all English alphabets in capital and small cases, digits from 0 to 9. The previous methods use synthetic mixed samples created by joining different parts of the images from the UWA WIHSI dataset.Each document consists of real mixed samples written withdifferent pens and by different writers with a variety of mixing ratios of inks and writers for forensic analysis.The standard A4 pages, each weighing 70 gs and manufactured by “AA” company, are used for data collection. The handwritten notes written by each subject with different pens are annotated in rectangular boxes. This dataset can be used for several tasks related to hyperspectral document image analysis and document forensic analysis including, handwritten optical character recognition, ink mismatch detection, writer identification at sentence, word, and character-level, handwriting-based gender classification, handwriting-based age prediction, handwritten word segmentation, and word generation. This dataset was designed and collected by the research team at the Artificial intelligence and Computer Vision Lab (iVision), Institute of Space Technology, Pakistan, and the hyperspectral images were acquired through imaging spectroscopy in the visible wavelength range at Wageningen University & Research, the Netherlands.

This article presents a handwritten Arabic alphabets, words and paragraphs dataset (AHAWP). The dataset contains 65 different Arabic alphabets (with variations on begin, end, middle and regular alphabets), 10 different Arabic words (that encompass all Arabic alphabets) and 3 different paragraphs. The dataset was collected anonymously from 82 different users. Each user was asked to write each alphabet and word 10 times. A userid uniquely but anonymously identifies the writer of each alphabet, word and paragraph. In total, the dataset consists of 53199 alphabet images, 8144 words images and 241 paragraphs images. This dataset can be used for multiple purposes. It can be used for optical handwriting recognition of alphabets and words. It can also be used for writer identification (or verification) of handwritten Arabic text. It is also possible to evaluate difference in writing styles of isolated alphabets as compared to the same alphabet written as part of the word or in paragraph by the same user using this dataset. The dataset is publicly available at https://data.mendeley.com/datasets/2h76672znt/1.

Writer identification (WI) is a typical pattern recognition problem with the goal of recognizing the writer of a text from images of his or her handwriting. For handwriting-based applications, a new approach is required that can confirm the writer based on a very small amount of available text. However, due to the limited number of characters and the diverse contents of the available samples, it is still challenging to achieve a high recognition rate on large-scale databases. To solve this problem, this paper proposes an efficient method for offline text-independent WI using redundant writing patterns and dual-factor analysis of variance (DF-ANOVA). In this method, a search for writing patterns in a limited number of handwritten texts is first conducted to generate meaningful codebooks. Then, these writing patterns are described by means of the simplified Wigner distribution function (WDF) and improved directional index histogram (DIH) descriptors to extract both structural and texture features, and the two corresponding feature distances are integrated by means of a fuzzy integral rule to make the final decision. The Fisher discriminant ratio (FDR) is used for feature selection, and DF-ANOVA is used to eliminate the influence of factors associated with the labels of samples on the feature distance. The results of evaluations on the IAM (96.92%), Firemaker (96.4%), and Uyghur2016 (100%) databases illustrate that this system shows strong robustness to an increasing number of writers and a decreasing number of words in the sample texts. [ABSTRACT FROM AUTHOR]

Writer identification (WI) based on handwritten text structures is typically focused on digital characteristics, with letters/strokes representing the information acquired fromthe current research in the integration of individual writing habits/styles. Previous studies have indicated that a word's attributes contribute to greater recognition than the attributes of a character or stroke. As a result of the complexity of Arabic handwriting, segmenting and separating letters and strokes from a script poses a challenge in addition to WI schemes. In this work, we propose new texture features for WI based on text. The histogram of oriented gradient (HOG) features are modified to extract good features on the basis of the histogram of the orientation for different angles of texts. The fusion of these features with the features of convolutional neural networks (CNNs) results in a good vector of powerful features. Then, we reduce the features by selecting the best ones using a genetic algorithm. The normalization method is used to normalize the features and feed them to an artificial neural network classifier. Experimental results show that the proposed augmenter enhances the results for HOG features and ResNet50, as well as the proposed model, because the amount of data is increased. Such a large data volume helps the system to retrieve extensive information about the nature of writing patterns. The affective result of the proposed model for whole paragraphs, lines, and sub words is obtained using different models and then compared with those of the CNN and ResNet50. The whole paragraphs produce the best results in all models because they contain rich information and the model can utilize numerous features for different words. The HOG and CNN features achieve 94.2% accuracy for whole paragraphs with augmentation, 83.2% of accuracy for lines, and 78% accuracy for sub words. Thus, this work provides a system that can identify writers on the basis of their handwriting and builds a powerful model that can help identify writers on the basis of their sentences, words, and sub words. [ABSTRACT FROM AUTHOR]

For text-independent writer identification(WI) tasks, this paper proposes a novel method based on semantic codebooks and two effective feature descriptors. This method requires constructing semantic codebooks consisting of various subimages and their labels. Moment features and high-order correlation functions are used to describe handwriting style, and the standard Euclidean distance function is used for distance measurement. Then, two-way analysis of variance(TW-ANOVA) is used to filter out character factors that affect identification accuracy. Finally, a combined measure obtained from multiple classifiers based on a fuzzy integral rule is used to determine the candidate writers. To verify the effectiveness of the system, experiments are performed using the Firemaker, IAM, CRUG-CN and Uyghur2016 databases. The evaluation results demonstrate better performance than most existing methods. [ABSTRACT FROM AUTHOR]

Writer identification has become a hot research topic in the fields of pattern recognition, forensic document analysis, the criminal justice system, etc. The goal of this research is to propose an efficient approach for writer identification based on online handwritten Kanji characters. We collected 47,520 samples from 33 people who wrote 72 online handwritten-based Kanji characters 20 times. We extracted features from the handwriting data and proposed a support vector machine (SVM)-based classifier for writer identification. We also conducted experiments to see how the accuracy changes with feature selection and parameter tuning. Both text-dependent and text-independent writer identification were studied in this work. In the case of text-dependent writer identification, we obtained the accuracy of each Kanji character separately. We then studied the text-independent case by considering some of the top discriminative characters from the text-dependent case. Finally, another text-dependent experiment was performed by taking two, three, and four Kanji characters instead of using only one character. The experimental results illustrated that SVM provided the highest identification accuracy of 99.0% for the text-independent case and 99.6% for text-dependent writer identification. We hope that this study will be helpful for writer identification using online handwritten Kanji characters.

Handwritten text recognition has been an ongoing attractive task to research in the field of document analysis and recognition with applications in handwriting forensics, paleography, document examination, and handwriting recognition. In the present research, an automatic method of writer recognition is presented using digitized images of unconstrained texts. Despite the increasing efforts by prior literature on the different methods used for the same purpose, such methods performance, particularly their accuracy, has not been promising, leaving plenty of room for improvements. This method made use of codebook-based writer characterization, with each writing sample represented by a group of computed features from a primary and secondary codebook. The writings were then represented through the computation of the probability of codebook patterns occurrence, and the probability distribution was employed for each writer's characterization. Writer identification process involved comparing two writings through the computation of the distances between their respective probability distribution. The study carried out experiments to determine the performance of the implemented method in light of rates of identification with the help of standard datasets, namely, KRDOH and IAM, the former being the most current and largest Kurdish handwritten datasets with 1076 writers, and the latter being a dataset containing 650 writers. The outcome of the experiments was promising with a rate of identification of 94.3%, with the proposed method outperforming the state-of-the-art methods by 2-3%. [ABSTRACT FROM AUTHOR]

Codebook‐based writer characterization is an effective technique that has been investigated in a number of recent studies on identification and verification of writers. These methods divide a set of writing samples into small units (fragments or graphemes) and cluster these patterns to produce a codebook. Writer of a handwritten sample is then characterized by the probability (distribution) of producing the codebook patterns. In most cases, a small subset of the database under study is employed to produce the codebook while the rest of the database is used in evaluations. This work aims to validate the hypothesis that the codebook simply serves as a representation space to compare different writings and, in most cases, the patterns in the codebook do not significantly influence the identification and verification performance. The hypothesis is validated by generating a number of codebooks using Greek, Arabic and Chinese handwritten samples. Moreover, codebooks using fragments of handwritten music scores, printed text and synthetic data are also investigated. Evaluations on three well‐known handwriting databases (CVL, BFL and IAM) validate the idea that, in general, the codebook patterns do not have a significant impact on characterizing writer from handwriting. [ABSTRACT FROM AUTHOR]

This article presents a Bangla handwriting dataset named BanglaWriting that contains single-page handwritings of 260 individuals of different personalities and ages. Each page includes bounding-boxes that bounds each word, along with the unicode representation of the writing. This dataset contains 21,234 words and 32,787 characters in total. Moreover, this dataset includes 5,470 unique words of Bangla vocabulary. Apart from the usual words, the dataset comprises 261 comprehensible overwriting and 450 handwritten strikes and mistakes. All of the bounding-boxes and word labels are manually-generated. The dataset can be used for complex optical character/word recognition, writer identification, handwritten word segmentation, and word generation. Furthermore, this dataset is suitable for extracting age-based and gender-based variation of handwriting.

Identification of a person from his writing is one of the challenging problems; however, it is not new. No one can repudiate its applications in a number of domains, such as forensic analysis, historical documents, and ancient manuscripts. Deep learning-based approaches have proved as the best feature extractors from massive amounts of heterogeneous data and provide promising and surprising predictions of patterns as compared with traditional approaches. We apply a deep transfer convolutional neural network (CNN) to identify a writer using handwriting text line images in English and Arabic languages. We evaluate different freeze layers of CNN (Conv3, Conv4, Conv5, Fc6, Fc7, and fusion of Fc6 and Fc7) affecting the identification rate of the writer. In this paper, transfer learning is applied as a pioneer study using ImageNet (base data-set) and QUWI data-set (target data-set). To decrease the chance of over-fitting, data augmentation techniques are applied like contours, negatives, and sharpness using text-line images of target data-set. The sliding window approach is used to make patches as an input unit to the CNN model. The AlexNet architecture is employed to extract discriminating visual features from multiple representations of image patches generated by enhanced pre-processing techniques. The extracted features from patches are then fed to a support vector machine classifier. We realized the highest accuracy using freeze Conv5 layer up to 92.78% on English, 92.20% on Arabic, and 88.11% on the combination of Arabic and English, respectively.

The handwriting of a person may vary substantially with factors, such as mood, time, space, writing speed, writing medium/tool, writing a topic, and so on. It becomes challenging to perform automated writer verification/identification on a particular set of handwritten patterns (e.g., speedy handwriting) of an individual, especially when the system is trained using a different set of writing patterns (e.g., normal speed) of that same person. However, it would be interesting to experimentally analyze if there exists any implicit characteristic of individuality which is insensitive to high intra-variable handwriting. In this paper, we study some handcrafted features and auto-derived features extracted from intra-variable writing. Here, we work on writer identification/verification from highly intra-variable offline Bengali writing. To this end, we use various models mainly based on handcrafted features with support vector machine and features auto-derived by the convolutional network. For experimentation, we have generated two handwritten databases from two different sets of 100 writers and enlarged the dataset by a data-augmentation technique. We have obtained some interesting results.

Writer's identification from a handwritten text is one of the most challenging machines learning problems because of the variable handwritten sources, various languages, the similarity between writer's pattern, context variation, and implicit characteristics of handwriting styles. In this paper, a combination of the deep and hand-crafted descriptor is utilized to learn patterns from the handwritten images. First, to do so, the local patches are extracted from the handwritten images. Then, these patches are simultaneously fed to deep and hand-crafted descriptors to generate the local descriptions. The extracted local features are then assembled to make the whole description matrix. Finally, by applying the vector of locally aggregated descriptors (VLAD) encoding on the description matrix, a 1-D feature vector is extracted to represent the writer's pattern. It is worthwhile to mention that the generated description does not rely on any language model or context information. Thus, the proposed approach is language and content independent. In addition, the proposed method does not have any restriction on the input length, hence, the writer's sample can be a passage, paragraph, line, sentence, or even a word. The obtained results on three public benchmark datasets of IAM, CVL, and Khatt indicate that the proposed method has a high-accuracy rate in writing identification task. Furthermore, the performance of the proposed method on CVL dataset using both German and English samples demonstrates that the proposed approach has a high capability in learning a writer's pattern from both languages at the same time.

Writer identification is a quite interesting research problem in the field of writing recognition due to an ambiguous written styles of different writers. This paper proposes a model that hybridises Convolutional Neural Network (CNN) and Multiclass-Support Vector Machine (MSVM) for getting a better accuracy in writer identification using English=Arabic handwriting samples. Deep identifying writer takes local handwritten image as input and CNN used for feature extraction then classified using MSVM classifier based on the extracted features from the CNN layers. The used CNN architecture was applied with multiple kernel sizes and each time the corresponding processing time and the identification accuracy was measured. The proposed system was applied over two publicly databases Khatt as an Arabic database and IAM as an English database and able to achieve an accuracy of around 99.8% for a set of 206 writers. The performance of the proposed system was compared with other existing writer identification systems. [ABSTRACT FROM AUTHOR]

The writer's identification/verification problem has traditionally been solved by analyzing complex biometric sources (text pages, paragraphs, words, signatures, etc.). This implies the need for pre-processing techniques, feature computation and construction of also complex classifiers. A group of simple graphemes (" S ", " ∩ ", " C ", " ∼ " and " U ") has been recently introduced in order to reduce the structural complexity of biometric sources. This paper proposes to analyze the images of simple graphemes by means of Convolutional Neural Networks. In particular, the AlexNet, VGG-16, VGG-19 and ResNet-18 models are considered in the learning transfer mode. The proposed approach has the advantage of directly processing the original images, without using an intermediate representation, and without computing specific descriptors. This allows to dramatically reduce the complexity of the simple grapheme processing chain and having a high hit-rate of writer identification performance. [ABSTRACT FROM AUTHOR]

In the framework of palaeography, the availability of both effective image analysis algorithms, and high-quality digital images has favored the development of new applications for the study of ancient manuscripts and has provided new tools for decision-making support systems. The quality of the results provided by such applications, however, is strongly influenced by the selection of effective features, which should be able to capture the distinctive aspects to which the paleography expert is interested in. This process is very difficult to generalize due to the enormous variability in the type of ancient documents, produced in different historical periods with different languages and styles. The effect is that it is very difficult to define standard techniques that are general enough to be effectively used in any case, and this is the reason why ad-hoc systems, generally designed according to paleographers' suggestions, have been designed for the analysis of ancient manuscripts. In recent years, there has been a growing scientific interest in the use of techniques based on deep learning (DL) for the automatic processing of ancient documents. This interest is not only due to their capability of designing high-performance pattern recognition systems, but also to their ability of automatically extracting features from raw data, without using any a priori knowledge. Moving from these considerations, the aim of this study is to verify if DL-based approaches may actually represent a general methodology for automatically designing machine learning systems for palaeography applications. To this purpose, we compared the performance of a DL-based approach with that of a "classical" machine learning one, in a particularly unfavorable case for DL, namely that of highly standardized schools. The rationale of this choice is to compare the obtainable results even when context information is present and discriminating: this information is ignored by DL approaches, while it is used by machine learning methods, making the comparison more significant. The experimental results refer to the use of a large sets of digital images extracted from an entire 12th-century Bibles, the "Avila Bible". This manuscript, produced by several scribes who worked in different periods and in different places, represents a severe test bed to evaluate the efficiency of scribe identification systems. [ABSTRACT FROM AUTHOR]

Handwriting is a unique thing that produced differently for each person. Handwriting has a characteristic that remain the same with single writer, so a handwriting can be used as a variable in biometric systems. Each person have a different form of handwriting style but with a small possibility that same characters have something commons. We propose a handwriting identification method using sentence segmented handwriting forms. Sentence form is used to get more complete handwriting characteristics than using a single characters or words. Dataset used is divided into three categories of images, binary, grayscale, and inverted binary. All datasets have same image with different in color and consist of 100 class. Transfer learning used in this paper are pre-trained model VGG19. Training was conducted in 100 epochs. Highest result is grayscale images with genuince acceptance rate of 92.3% and equal error rate of 7.7%. [ABSTRACT FROM AUTHOR]

A biometric identification system based on single and multiple modalities has been an evolving concept for solving criminal issues, security and privacy maintenance and for checking the authentication of an individual. The writer identification system is a type of biometric identification in which handwriting of an individual is taken as a biometric identifier. It is a system in which the writer can be identified based on his handwritten text. These systems employ machine learning and pattern recognition algorithms for the generation of a framework. In this paper, the authors have presented a novel system for the writer identification based upon the pre-segmented characters of Devanagari script and also presenting comprehensive state-of-the-art work. The experiment is performed on the corpus consisting of five copies of each character of Devanagari script written by 100 different writers, selected randomly at the public places and consisting of total 24,500 samples of Devanagari characters. Four feature extraction methodologies such as zoning, diagonal, transition and peak extent-based features and classification methods such as k-NN and linear SVM are used with identification accuracy of 91.53% when using zoning, transition and peak extent-based features with a linear SVM classifier. [ABSTRACT FROM AUTHOR]

In this paper, we work on intra-variable handwriting, where the writing samples of an individual can vary significantly. Such within-writer variation throws a challenge for automatic writer inspection, where the state-of-the-art methods do not perform well. To deal with intra-variability, we analyze the idiosyncrasy in individual handwriting. We identify/verify the writer from highly idiosyncratic text-patches. Such patches are detected using a deep recurrent reinforcement learning-based architecture. An idiosyncratic score is assigned to every patch, which is predicted by employing deep regression analysis. For writer identification, we propose a deep neural architecture, which makes the final decision by the idiosyncratic score-induced weighted average of patch-based decisions. For writer verification, we propose two algorithms for patch-fed deep feature aggregation, which assist in authentication using a triplet network. The experiments were performed on two databases, where we obtained encouraging results. [ABSTRACT FROM AUTHOR]

Writer identification based on a small amount of text is a challenging problem. In this paper, we propose a new benchmark study for writer identification based on word or text block images which approximately contain one word. In order to extract powerful features on these word images, a deep neural network, named FragNet, is proposed. The FragNet has two pathways: feature pyramid which is used to extract feature maps and fragment pathway which is trained to predict the writer identity based on fragments extracted from the input image and the feature maps on the feature pyramid. We conduct experiments on four benchmark datasets, which show that our proposed method can generate efficient and robust deep representations for writer identification based on both word and page images. [ABSTRACT FROM AUTHOR]

Writer identification is a quite interesting research problem in the field of writing recognition due to an ambiguous written styles of different writers. This paper proposes a model that hybridises Convolutional Neural Network (CNN) and Multiclass-Support Vector Machine (MSVM) for getting a better accuracy in writer identification using English/Arabic handwriting samples. Deep identifying writer takes local handwritten image as input and CNN used for feature extraction then classified using MSVM classifier based on the extracted features from the CNN layers. The used CNN architecture was applied with multiple kernel sizes and each time the corresponding processing time and the identification accuracy was measured. The proposed system was applied over two publicly databases Khatt as an Arabic database and IAM as an English database and able to achieve an accuracy of around 99.8% for a set of 206 writers. The performance of the proposed system was compared with other existing writer identification systems. [ABSTRACT FROM AUTHOR]

Identifying and verifying the identity of people based on scanned images of handwritten documents is an applicable biometric modality with applications in forensic and historic document investigation, and it is an important study area within the research field of behavioral biometrics. Despite this, there are few studies in this field. Furthermore, there are very few standard datasets for identifying and verify handwritten documents. Also, handwritten documents lose their character during time because of ink spread and drying. Therefore, it is necessary to provide a method that can identify and verify handwritten documents under various uncertainties. In this study, a text-independent writer identification and verification model in offline state under different experimental conditions is developed using a combination of Deep Type-2 Fuzzy architecture and Transfer Learning networks (DT2F-TLNet). So, a right-to-left dataset has been collected. The proposed DT2F-TLNet model is validated using both the designed dataset and other benchmark datasets. The proposed model is distinguished by the fact that it is developed to be independent of the textual content of the handwritten cases and can be used for various languages. The study's findings show that the developed DT2F-TLNet model can learn properties from heterogeneous handwriting data and results in higher accuracy than other comparable approaches. [ABSTRACT FROM AUTHOR]

Writer Identification has gained increasing importance in the scientific community in recent years. In this paper, we propose an approach based on the combination of local textural descriptors and encoding methods (VLAD and Triangulation Embedding). The tests carried out in the bilingual LAMIS dataset made it possible to reach 100% in the Arabic version and 100% in the French version.