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Text entry for smartwatches is a useful service for many applications like sending text messages and replying emails. Traditional touchscreen-based approaches are two-handed text entry methods, that could be cumbersome when the user is performing other tasks with one hand. Therefore, we propose AirText, the first one-handed text entry method which achieves accurate and practical handwriting in the air for commercial smartwatches. By analyzing the inertial readings from the smartwatch worn on the wrist, AirText is able to accurately recognize the in-air handwritten characters. However, the wrist movements, which produce the inertial readings, are harmful to the user to focus on the screen. In order to address this challenge, AirText uses a novel cross-modal supervision design to achieve accurate character recognition from the small wrist movements. AirText further includes a novel word recommendation method to speed up the text entry. We implement AirText on five smartwatches and evaluate its performance extensively with eight volunteers and more than 25,000 in-air handwritten characters. Results show that AirText outperforms two baselines methods and achieves comparable text entry speed as two-handed approaches.

Handwritten Text Recognition has achieved an impressive performance in public benchmarks. However, due to the high inter- and intra-class variability between handwriting styles, such recognizers need to be trained using huge volumes of manually labeled training data. To alleviate this labor-consuming problem, synthetic data produced with TrueType fonts has been often used in the training loop to gain volume and augment the handwriting style variability. However, there is a significant style bias between synthetic and real data which hinders the improvement of recognition performance. To deal with such limitations, we propose a generative method for handwritten text-line images, which is conditioned on both visual appearance and textual content. Our method is able to produce long text-line samples with diverse handwriting styles. Once properly trained, our method can also be adapted to new target data by only accessing unlabeled text-line images to mimic handwritten styles and produce images with any textual content. Extensive experiments have been done on making use of the generated samples to boost Handwritten Text Recognition performance. Both qualitative and quantitative results demonstrate that the proposed approach outperforms the current state of the art., Comment: Accepted to TPAMI

The subtleties of human perception, as measured by vision scientists through the use of psychophysics, are important clues to the internal workings of visual recognition. For instance, measured reaction time can indicate whether a visual stimulus is easy for a subject to recognize, or whether it is hard. In this paper, we consider how to incorporate psychophysical measurements of visual perception into the loss function of a deep neural network being trained for a recognition task, under the assumption that such information can enforce consistency with human behavior. As a case study to assess the viability of this approach, we look at the problem of handwritten document transcription. While good progress has been made towards automatically transcribing modern handwriting, significant challenges remain in transcribing historical documents. Here we describe a general enhancement strategy, underpinned by the new loss formulation, which can be applied to the training regime of any deep learning-based document transcription system. Through experimentation, reliable performance improvement is demonstrated for the standard IAM and RIMES datasets for three different network architectures. Further, we go on to show feasibility for our approach on a new dataset of digitized Latin manuscripts, originally produced by scribes in the Cloister of St. Gall in the the 9th century.

The recognition of online handwriting is a vital application of pattern recognition, which involves the extraction of spatial and temporal information of handwritten patterns, and understanding the handwritten text while writing on the digital surface. Although, online handwriting recognition is a mature but exciting and fast developing field of pattern recognition, the same is not true for many of the Indic scripts. Gurmukhi is one of such popular scripts of India, and online handwriting recognition issues for larger units as words or sentences largely remained unexplored for this script till date. The existing study and first ever attempt for online handwritten Gurmukhi word recognition has relied upon the widely used hidden Markov model. This existing study evaluated against and performed very well in their chosen metrics. But, the available online handwritten Gurmukhi word recognition system could not obtain more than 90% recognition accuracy in data dependent environment too. The present study provided benchmark results for online handwritten Gurmukhi word recognition using deep learning architecture convolutional neural network, and obtained above 97% recognition accuracy in data dependent mode of handwriting. The previous Gurmukhi word recognition system followed the stroke based class labeling approach, whereas the present study has followed the word based class labeling approach. Present Online handwritten Gurmukhi word recognition results are quite satisfactory. Moreover, the proposed architecture can be used to improve the benchmark results of online handwriting recognition of several major Indian scripts. Experimental results demonstrated that the deep learning system achieved great results in Gurmukhi script and outperforms existing results in the literature.

Existing decision support system frameworks for diagnosing Parkinson's disease (PD) through handwriting, speech, or gait characteristics share very similar pipelines. Although in some cases, patient data can be captured by commercially available devices, specialized devices or even custom-made prototypes are often required for such tasks. Captured data are used for extracting features that are carefully designed on the basis of domain and problem knowledge. These features are then fed to classifiers that provide a final decision. In this article, we present an approach in which end-to-end processing by a convolutional neural network (CNN) is utilized to diagnose PD from handwriting images, without the use of additional signals. This eliminates any need for specialized devices or feature engineering. To improve the performance of the proposed pretrained CNN, we propose the idea of multiple fine tuning to bridge the gap between semantically different source and target datasets and facilitate more efficient transfer learning. The proposed architecture, which is based on multiple fine tuning and an ensemble of multiple-fine-tuned CNNs, achieves 94.7% accuracy in the classification of PD from offline handwriting.

Numeral recognition plays a crucial role in creating automated systems such as posting address sorting and license plate recognition. Nowadays, numeral recognition systems which have the capability of recognising multiple languages are highly beneficial due to growing international correspondence and transactions, especially in multilingual countries where several languages are used simultaneously. Therefore, handwritten numeral recognition is more challenging than printed numeral recognition due to having different and complex handwriting styles. Hence, developing a multilingual handwritten system is considered as an important and debatable issue. We address this issue by proposing a language-independent model based on a robust CNN . Our proposed model is composed of language recognition and digit recognition, which aims to handle the recognition of multi-script images. We used transfer-learning in the proposed system to enhance the image quality and consequently the recognition performance. Extensive experiments were conducted to verify the effectiveness of both language and digit recognition procedures. The proposed system was tested with six different languages. The results showed an average accuracy of up to 99.8% for recognising various languages and the associated digits. The robustness and design procedure of the proposed model created a cost-effective extension for recognition of handwritten numerals in other languages.

Production time of stroke gestures is a fundamental measure of user performance with graphical user interfaces. However, production time represents an overall quantification of the user's gesture articulation process and therefore provides an incomplete picture of such process. Moreover, previous approaches assumed stroke gestures as synchronous point sequences, when most gesture-driven applications have to deal with asynchronous point sequences. Furthermore, deep generative models of human handwriting ignore the temporal information, thereby missing a key component of the user's gesture articulation process. To solve these issues, we introduce Ditto , a sequence-to-sequence deep learning model that estimates the velocity profile of any stroke gesture using spatial information only, providing thus a fine-grained estimation of the moment-by-moment behavior of the user's articulation performance. We show that this unique capability makes Ditto remarkably accurate while handling gestures of any type: unistrokes, multistrokes, and multitouch gestures. Our model, code, and associated web application are available as open source software.

An approach is presented that generalize OCR task including polish letters using deep learning technique. The paper extends EMNIST dataset so that two new classes of polish diacritics “Ą” and “C” are attached. Using this new dataset and deep learning technique one can analyze sensitives of standard as well as presented extended approach with different parameters of convolutional neural network. The analysis of the results shows that the shadows and noises that Polish characters, leave with their hooks leads, can be properly recognized and two similar letters “A” and “Ą” can be distinguished by convolutional neural network. On the other hand a neural network trained on dataset without Polish characters do not treat letters “Ą” and “C” properly.

The present research was aimed at finding out the relative effect of advancement in photocopier technology on the analysis of handwriting line quality features in multi-generational photocopies. Five subsequent multi-generational photocopies of 50 signature samples from different individuals which were produced by 75 black and white photocopiers were used for line quality assessments. Variations in line quality features in different photocopy generations, such as: smooth edges or ragged edges, broken or continuous stroke, fine tapering changes, and some identification details of features like pen lifts, retouching, and hesitation were carefully observed in sample photocopies, which were recorded according to the brand and model of photocopier machines, as well as their size and speed. The results of the study revealed that raggedness in line was often observed in third generation (G3) and higher generations, in samples produced by some low speed copiers (copying speed < 30 cpm) or very high speed copiers (copying speed ≤ 80 cpm). However, their line quality was still found to be good enough for handwriting analysis up to the fifth generation (G5) in photocopy samples produced by some medium speed copiers (copying speed 30 to 50 cpm). It was, however, observed that, usually, continuity of line and smoothness of line were appreciably lost in G3 and higher generations in photocopies which were produced by portable desktop printers. Exceptionally, certain artifacts that are usually found in some higher generation copies were conspicuously absent in all photocopy generations (G1–G5) which were produced by some advanced third generation photocopiers. Hopefully, the information obtained from the study will be found useful to document examiners when examining certain cases involving multi-generations of photocopier reproductions.

Graphology is a method of assessment of human personality from individual's handwriting style. The personality in terms of various attributes like fear, honesty, work habits, social skills etc. can be profiled using Graphology. The personality profiling is based on various features like line spacing, margins, slant, letter size, text density etc. Letter t and i are very important personality trait indicators in graphology. Letter t is an indicator of will in individuals. Letter i is an indicator of social connectivity in individuals. This work proposes a deep learning classifier to classify the specific type of t and i patterns. Convolutional neural network is trained to extract the t and i features. The features extracted from t and i handwriting patterns are classified using the ensemble of Deep learning classifier. The performance of the proposed ensemble classification is tested against different handwritten documents. The proposed solution is able to achieve about 90% classification accuracy

Here we introduce Local Topological Recurrence Analysis (LoTRA), a simple computational approach for analyzing time-series data. Its versatility is elucidated using simulated data, Parkinsonian gait, and in vivo brain dynamics. We also show that this algorithm can be used to build a remarkably simple machine-learning model capable of outperforming deep-learning models in detecting Parkinson’s disease from a single digital handwriting test.

Recent advances in complex automated handwriting identification systems have led to a lack of understandability of these systems' computational processes and features by the forensic handwriting examiners that they are designed to support. To mitigate this issue, this research studied the relationship between two systems: FLASH ID® , an automated handwriting/black box system that uses measurements extracted from a static image of handwriting, and MovAlyzeR® , a system that captures kinematic features from pen strokes. For this study, 33 writers each wrote 60 phrases from the London Letter using cursive writing and handprinting, which led to thousands of sample pairs for analysis. The dissimilarities between pairs of samples were calculated using two score functions (one for each system). The observed results indicate that dissimilarity scores based on kinematic spatial-geometric pen stroke features (e.g., amplitude and slant) have a statistically significant relationship with dissimilarity scores obtained using static, graph-based features used by the FLASH ID® system. Similar relationships were observed for temporal features (e.g., duration and velocity) but not pen pressure, and for both handprinting and cursive samples. These results strongly imply that both the current implementation of FLASH ID® and MovAlyzeR® rely on similar features sets when measuring differences in pairs of handwritten samples. These results suggest that studies of biometric discrimination using MovAlyzeR® , specifically those based on the spatial-geometric feature set, support the validity of biometric matching algorithms based on FLASH ID® output.

In handwriting recognition, traditional systems have relied heavily on handcrafted features and a massive amount of prior data and knowledge. Deep learning techniques have been the focus of research in the field of handwriting digit recognition and have achieved breakthrough performance in the last few years for knowledge extraction and management. KM and knowledge pyramid helps the project with its relationship with big data and IoT. The layers were selected randomly by which the performance of all the cases was found different. Data layers of the knowledge pyramid are formed by the sensors and input devices, whereas knowledge layers are the result of knowledge extraction applied on data layers. The knowledge pyramid and KM helps in making the use of IoT and big data easily. In this manuscript, the knowledge management principles capture the handwritten gestures numerically and get it recognized correctly by the software. The application of AI and DNN has increased the acceptability significantly. The accuracy is better than other available software on the market.

Parkinson's disease (PD) is the second most common neurodegenerative disorder disease right after Alzheimer's and the most common movement disorder for elderly people. It is characterized as a progressive loss of muscle control, which leads to trembling characterized by uncontrollable shaking, or (tremors) in different parts of the body. In recent years, deep learning (DL) models achieved significant progress in automatic speech recognition, however, limited studies addressed the problem of distinguishing people with PD for further clinical diagnosis. In this paper, an approach for the early detection of patients with PD using speech features was proposed, a recurrent neural network (RNN) with long short-term memory (LSTM) is applied with the batch normalization layer and adaptive moment estimation (ADAM) optimization algorithm used after the network hidden layers to improve the classification performance. The proposed approach is applied with 2 benchmark datasets of speech features for patients with PD and healthy control subjects. The proposed approach achieved an accuracy of 95.8% and MCC=92.04% for the testing dataset. In future work, we aim to increase the voice features that will be worked on and consider using handwriting kinematic features.

Arabic Handwritten Text Recognition (AHTR) based on deep learning approaches remains a challenging problem due to the inevitable domain shift like the variability among writers’ styles and the scarcity of labelled data. To alleviate such problems, we investigate in this paper different domain adaptation strategies of AHTR system. The main idea is to exploit the knowledge of a handwriting source domain and to transfer this knowledge to another domain where only few labelled data are available. Different writer-dependent and writer-independent domain adaptation strategies are explored using a convolutional neural networks (CNN) and Bidirectional Long Short Term Memory (BSTM) - connectionist temporal classification (CTC) architecture. To discuss the interest of the proposed techniques on the target domain, we have conducted extensive experiments using three Arabic handwritten text datasets, mainly, the MADCAT, the AHTID/MW and the IFN/ENIT. Concurrently, the Arabic handwritten text dataset KHATT was used as the source domain. The obtained results prove the effectiveness of the proposed strategies specially when considering the writer’s information during the supervised adaptation process.

Handwriting is used to distribute information among people. To access this information for further analysis the page needs to be optically scanned and converted to machine recognizable form. Due to unconstrained writing styles along with connected and overlapping characters, handwriting recognition remains a challenging task. Most of the methods in the literature use lexicon-based approaches and train their models on large datasets having near 50 K word samples to achieve good results. This results in high computational requirements. While these models use around 50 K words in their dictionary when recognizing handwritten English text, the actual number of words in the dictionary is much higher than this. To this end, we propose a handwriting recognition technique to recognize handwritten English text based on a YOLOv3 object recognition model that is lexicon-free and that performs sequential character detection and identification with a low number of training samples (only 1200 word images). This model works well without any dependency on writers’ style of writing. This is tested on the IAM dataset and it is able to achieve 29.21% Word Error Rate and 9.53% Character Error Rate without a predefined vocabulary, which is on par with the state-of-the-art lexicon-based word recognition models.

Online signals are rich in dynamic features such as trajectory chronology, velocity, pressure and pen up/down movements. Their offline counterparts consist of a set of pixels. Thus, online handwriting recognition accuracy is generally better than offline. In this paper, we propose an original framework for recovering temporal order and pen velocity from offline multi-lingual handwriting. Our framework is based on an integrated sequence-to-sequence attention model. The proposed system involves extracting a hidden representation from an image using a convolutional neural network (CNN) and a bidirectional gated recurrent unit (BGRU), and decoding the encoded vectors to generate dynamic information using a BGRU with temporal attention. We validate our framework using an online recognition system applied to a benchmark Latin, Arabic and Indian On/Off dual-handwriting character database. The performance of the proposed multi-lingual system is demonstrated through a low error rate of point coordinates and high accuracy system rate.

Text entry on a smartwatch is challenging due to its small form factor. Handwriting recognition using the built-in sensors of the watch (motion sensors, microphones, etc.) provides an efficient and natural solution to deal with this issue. However, prior works mainly focus on individual letter recognition rather than word recognition. Therefore, they need users to pause between adjacent letters for segmentation, which is counter-intuitive and significantly decreases the input speed. In this paper, we present 'Write, Attend and Spell' (WriteAS), a word-level text-entry system which enables free-style handwriting recognition using the motion signals of the smartwatch. First, we design a multimodal convolutional neural network (CNN) to abstract motion features across modalities. After that, a stacked dilated convolutional network with an encoder-decoder network is applied to get around letter segmentation and output words in an end-to-end way. More importantly, we leverage a multi-task sequence learning method to enable handwriting recognition in a streaming way. We construct the first sequence-to-sequence handwriting dataset using smartwatch. WriteAS can yield 9.3% character error rate (CER) on 250 words for new users and 3.8% CER for words unseen in the training set. In addition, WriteAS can handle various writing conditions very well. Given the promising performance, we envision that WriteAS can be a fast and accurate input tool for smartwatch.

The paper highlights the current state of the theory and practice of forensic handwriting analysis of signatures, main provisions and definitions. Topical issues of the study of signatures as a kind of small volume handwriting objects are considered. The problems of the study of a signature produced by using high-tech devices, i.e. plotters, or draft machines, are touched upon. The authors carry out an analysis of such objects taking into account the pressure characteristics of the signature, the differentiation of the original signature and its imitation. The problems of the study of signatures made with the help of copiers and the ways of their solution are considered. Some topical problems of the theory and practice of forensic handwriting examination are presented, the need to adapt the theoretical provisions of certain private methods to modern handwriting objects is indicated. In addition, the paper deals with the use of special knowledge of experts in the field of forensic handwriting studies and technical and forensic examination of documents.

Bakmi Golek is a restaurant that has been around for a long time and not only provides noodles but other Chinese food. The ordering system at Bakmi Golek Rawamangun still uses a manually, so the customer assess there are still several problems, including the food ordering process still using handwriting. The data storages are still using paper media, data search will take a long time because it must have to find a paper one by one on an archive, and making reports still require a lot of writing, so the data that has been stored in the archive will be lost. The purpose of this research is to provide effective and efficient solutions through KMS to Bakmi Golek Rawamangun employees in order to help improve performance while doing work, make KMS on there, to facilitate users in carrying out buying and selling transactions, data processing and checking sales reports and for presents a good ordering system by providing convenience and information for consumers in the food ordering process. The method used in this study is a research and development method with system development using the waterfall method. The result of this research is an application system of order and food information based on knowledge management system.

In the era of pervasively connected and sensed Internet of Things, many of our interactions with machines have been shifted from conventional computer keyboards and mouses to hand gestures and writing in the air. While gesture recognition and handwriting recognition have been well studied, many new methods are being investigated to enable pervasive handwriting tracking. Most of the existing handwriting tracking systems either require cameras and handheld sensors or involve dedicated hardware restricting user convenience and the scale of usage. In this article, we present mmWrite , the first high-precision passive handwriting tracking system using a single commodity millimeter-wave (mmWave) radio. Leveraging the short wavelength and large bandwidth of 60-GHz signals and the radar-like capabilities enabled by the large phased array, mmWrite transforms any flat region into an interactive writing surface that supports handwriting tracking at millimeter accuracy. MmWrite employs an end-to-end pipeline of signal processing to enhance the range and spatial resolution limited by the hardware, boost the coverage, and suppress interference from backgrounds and irrelevant objects. We implement and evaluate mmWrite on a commodity 60-GHz device. The experimental results show that mmWrite can track a finger/pen with a median error of 2.8 mm and thus can reproduce handwritten characters as small as 1 cm $\times $ 1 cm, with a coverage of up to 8 m2 supported. With minimal infrastructure needed, mmWrite promises ubiquitous handwriting tracking for new applications in the field of human–computer interactions.

Background: The performance of biometric modalities based on things done by the subject, like signature and text-based recognition, may be affected by the subject state. Fatigue is one of the conditions that can significantly affect the outcome of handwriting tasks. Recent research has already shown that physical fatigue produces measurable differences in some features extracted from common writing and drawing tasks. It is important to establish to which extent physical fatigue contributes to the intra-person variability observed in these biometric modalities and also to know whether the performance of recognition methods is affected by fatigue. Goal: In this paper we assess the impact of fatigue on intra-user variability and on the performance of signature-based and text-based writer recognition approaches encompassing both identification and verification. Methods: Several signature and text recognition methods are considered and applied to samples gathered after different levels of induced fatigue, measured by metabolic and mechanical assessment and, also by subjective perception. The recognition methods are Dynamic Time Warping and Multi Section Vector Quantization, for signatures, and Allographic Text-Dependent Recognition for text in capital letters. For each fatigue level, the identification and verification performance of these methods is measured. Results: Signature shows no statistically significant intra-user impact, but text does. On the other hand, performance of signature-based recognition approaches is negatively impacted by fatigue whereas the impact is not noticeable in text-based recognition, provided long enough sequences are considered.

Gender classification system is an automated, challenging, and efficacious system due to the analogous vision of males and females with handwriting. Gender classification is a binary problem that is based on physiological and behavioral biometric traits. It is widely used for forensic-based investigations, autopsy determination, suspected areas, and questioned documents. The proposed study has been considered for the behavioral biometric trait, i.e., handwriting in the Gurumukhi script. The novelty of this study can be seen from the three perspectives, i.e., hybridization of feature extraction techniques, principal component analysis (PCA) for dimensionality reduction, and hybridization of classification techniques. Zoning, Diagonal, Transition, and Peak Extent-based feature extractions were implemented followed by their hybridization. The classification techniques such as Decision Trees, Random Forest, and Extreme Gradient Boosting classifiers were experienced with the hybridized approach using a majority voting scheme. Also, the comparison and critical analysis of gender classification accuracy with CPU elapsed time has also been presented before and after implementing PCA. There are many novel ideas for the researchers such as gender classification through online handwriting, prediction of age, personality, state, nationality, and stress prediction through handwriting, and further gender classification problem can be enhanced to third or trans-gender too using handwriting as a biometric modality. The system can be implemented with many other Indic scripts and presenting novel track in the handwriting-based researches.

In this paper, we introduce a large scale dataset, called HKR, to address challenging detection and recognition problems of handwritten Russian and Kazakh text in the scanned documents. We present a new Russian and Kazakh database (with about 95% of Russian and 5% of Kazakh words/sentences respectively) for offline handwriting recognition. A few pre-processing and segmentation procedures have been developed together with the database. The database is written in Cyrillic and shares the same 33 characters. Besides these characters, the Kazakh alphabet also contains 9 additional specific characters. This dataset is a collection of forms. The sources of all the forms in the datasets were generated by LaTeXwhich subsequently was filled out by persons with their handwriting. The database consists of more than 1500 filled forms. There are approximately 63000 sentences, more than 715699 symbols produced by approximately 200 different writers. It can serve researchers in the field of handwriting recognition tasks by using deep and machine learning. For experiments, we used several popular text recognition methods for word and line recognition like CTC-based and attention-based methods. The results indicate the diversity of HKR. The dataset is available at https://github.com/abdoelsayed2016/HKR_Dataset .

Identifying the formation time of a document is a problem forensic document examiners often face. It is understood that intersections with different intersecting sequences made by the same laser printer and the same gel pen are distinct from each other under coaxial light, and the appearance of an oil film or bright metallic marks in the regions of interest can be used as the basis to identify that the ink is above the toner. However, the characteristics of intersections may be influenced by a number of factors. Studying the characteristics of the intersections formed by laser-printed graphics and handwriting strokes made with a gel pen in different intersecting sequences can help us determine the formation sequence of each part on a questioned document. In this manuscript, samples were made using three pen types and two laser printers. A Leica stereo microscope M205A was used to capture the apparent morphology of the intersections. Microspectrophotometry spectra of all intersections were collected in reflectance mode using a video spectral comparator 6000 (VSC6000), and the resultant data were measured and analyzed in Statistical Product and Service Solutions 26.0 (SPSS). The results showed that 92.5% absolute accuracy was achieved from blind tests, and the intersecting sequence can be determined using the optical data of the intersections with an accuracy of up to 97.5% through discriminant analysis under ideal conditions.

The present study focuses on the identification of individual characteristics in handwriting for the purpose of estimating the gender of the writer. A total of 150 individuals participated in the s...

At the present stage, the objects of forensic handwriting analysis are signatures performed with a significant gap in time. In this case, such objects are both signatures in studied documents, made at different intervals and studied signatures performed in relation to the comparative material with a time gap. Establishing stability of signature signs depending on performing time is possible taking into account characteristic signs of the stages of the signature handwriting formation. Since the factors causing changes in signatures are a lengthy process of forming the signature skill and the amount of signature practice (that is how often a person has to sign). While research on signatures that are at the formation stage, different signs, established together with significant coinciding signs canindicate a further improvement of movements. In personsof middle age (30-60 years old) who have a formed signature handwriting, significant changes do not occur over time until they stop practicing writing. While research on elderly people signatures along with the established coinciding signs, differing ones appear: signs of ataxia, decreased coordination of movements, low coherence of written signs, etc. These signs indicate degradation of movements due to physiological changes in the body in old age and a decrease in writing practice including practice of performing signature. Considering the above, research on signatures performed with a significant time gap is a rather complicated process that often causes difficulties when forensic expert evaluates revealed signs. Within the framework of this research paper, the authors focused on some topical issues existing in modern forensic expert practice of handwriting analyses of signatures performed with a significant time gap. The state of development of modern practical handwriting studies requires improvement of methodological approaches to this type of research. Currently, forensic experts are conducting R&D on the topic: “Improving research methods of signatures performed with a significant gap in time” which relevance is due to the need to improve forensic expert algorithm while handwriting research on signatures performed with a significant gap in time, as well as requirements of modern scientific standards.

In modern societies, handwritten identification is a great practical need and challenge for forensic sciences. Moreover, few research studies have focused on automatic offline handwritten document analysis. Also, there are very few standard datasets on handwritten document identification. In addition, handwritten documents lose their nature over time due to the spread and drying of ink. From this standpoint, the present study presents an offline writer identification system (in presence of the uncertainties such as different experimental conditions and environmental noises for more realistic assumptions) that is a critical need in forensic studies. For this purpose, a comprehensive right-to-left dataset is developed and devised by gathering data from 62 participants at different time intervals under different experimental conditions. This dataset is designed based on American Society for Testing and Materials (ASTM) standards. A Deep Convolutional Neural Network (DCNN) model based on modified pre-trained networks is designed and developed to extract features from raw data hierarchically. The proposed DCNN model is tested and investigated not only on the designed dataset but also on different datasets. One notable advantage of the present study is that it has made use of heterogeneous data. Another remarkable aspect of the proposed study is that the proposed DCNN model is independent of any specific languages which can be applied on various languages. The results of the study indicate that the proposed DCNN model can learn features hierarchically from the handwriting raw data and achieve higher accuracy than other comparative methods.

The recognition of handwritten Bangla digit is providing significant progress on optical character recognition (OCR). It is a very critical task due to the similar pattern and alignment of handwriting digits. With the progress of modern research on optical character recognition, it is reducing the complexity of the classification task by several methods, a few problems encounter during recognition and wait to be solved with simpler methods. The modern emerging field of artificial intelligence is the Deep Neural Network, which promises a solid solution to these few handwritten recognition problems. This paper proposed a fine regulated deep neural network (FRDNN) for the handwritten numeric character recognition problem that uses convolutional neural network (CNN) models with regularization parameters which makes the model generalized by preventing the overfitting. This paper applied Traditional Deep Neural Network (TDNN) and Fine regulated deep neural network (FRDNN) models with a similar layer experienced on BanglaLekha-Isolated databases and the classification accuracies for the two models were 96.25% and 96.99%, respectively over 100 epochs. The network performance of the FRDNN model on the BanglaLekha-Isolated digit dataset was more robust and accurate than the TDNN model and depend on experimentation. Our proposed method is obtained a good recognition accuracy compared with other existing available methods.

Following the correct stroke order while writing Chinese characters composed of strokes plays an important role in handwriting efficiency and quality, especially for early education. Most existing systems use image processing techniques for character and stroke order recognition, which is sensitive to lighting conditions. In this paper, we present the design, implementation and evaluation of SmartSO , which utilizes the inertial sensors of an off-the-shelf smartwatch for Chinese character and stroke order recognition. SmartSo first identifies the Chinese character written by the user, based on which SmartSo decides whether the stroke order is written correctly to help improve users' writing behavior. The biggest challenge for stroke order recognition is that some Chinese characters have repeated strokes (strokes of the same type), e.g., ‘本’ with two same horizontal strokes, and it is challenging to differentiate the writing order of such strokes given only the detected stroke composition (number and type of strokes). To mitigate this problem, we further analyze the hand movement between two adjacent strokes (referred to as direction motion) and propose a novel algorithm to recognize stroke order based on direction motion information. Finally, we build a fully functional prototype of SmartSO , and extensive experiments confirm its effectiveness and robustness.

The lack of large training data in the context of deep learning applications is a serious issue investigated by many studies that deal with the current challenge. In this paper, we introduce new data augmentation methods that generate more shape and dynamic variations to improve the performance of recognition systems using small datasets. Four data augmentation strategies are employed in our work. The first strategy employs the geometric methods that include: italicity angle, change of magnitude ratio, and baseline inclination angle. The second strategy applies a frequency treatment that attenuates or amplifies the trajectory high harmonics to generate handwriting modified styles. The third strategy employs the beta-elliptic model to extract a combined static and dynamic representation of the handwritten trajectory which undergoes limited random change around its parameters in order to generate more modified samples. The hybrid strategy consists of combining these strategies to maximize variations of the online handwriting trajectory (OHT). We evaluated our approach of data augmentation in the context of multi-lingual online handwriting recognition (OHR) tasks using end-to-end CNN architecture. Four databases; ADAB, ALTEC-OnDB, and Online_KHATT for Arabic script, and UNIPEN for Latin characters, are used to validate the proposed strategy. The obtained results show the effectiveness and the advantage of the adopted strategies compared with those registered before database extension or reported in the state-of-the-art systems.

Illegible handwriting of doctors in prescriptions causing difficulty for pharmacists in providing exact medicine to patients. Every year about thousands of patients losing their lives throughout the world due to doctors sloppy handwriting skills. The solution for this problem is a voice prescription application that uses speech-to-text technology to make flawless and legible prescriptions. The generated prescription can be shared with the patient either in a soft copy or hard copy format. This application also maintains the medical records of patients for future purposes and also provides the facility to book an appointment with a doctor. The integrated AIML (Artificial Intelligence Markup Language) chatbot suggests a patient determine the type of doctor to be consulted based on symptoms. And also it provides a diagnosis for minute health issues.

The specially-abled face a lot of issues when it comes to taking up exams. They require specific assistance for taking up their exams known as a scribe. However in today’s world finding a scribe is of great difficulty. Even if the scribe is found there are a lot of other practical difficulties while using a scribe to write exams. One such practical difficulty in using a scribe to write exams is that the scribe may not be aware of the technical terms used by the candidate. This causes loss of marks for the candidate if the scribe makes an error. The scribe should also have a legible handwriting which again is a problem. Our project aims at eliminating the need for a scribe and allowing the candidate itself to take up their exams by leveraging the latest technologies such as speech recognition and synthesis. This is achieved with the help of an android application, using which the questions can be stored in a database using administrator login. A separate candidate login is given. The question will be read out to the candidate and the answer will be taken as a voice response.

The research explains and analyzes the most important digital methods that depend on graphic design of elements and icons. These help illiterate people comprehend shapes, words, letters, and numbers, which greatly affect the process of understanding the matrix and the information available through the applications which are used. These systems allow diversification and distribution of elements and spaces using colors and consider how the recipient perceives this information. Further, the research sheds light on the role of electronic games and other applications that have become an integral part of the information design process in a graphic form that follows modern rules and regulations in this field. The research presents foundations of designing educational applications, some samples of literacy applications and displaying samples of speech recognition and handwriting applications, the main requirements for designing an electronic application. Requirements. The research also identifies the problem in the form of questions, and the research continued the explanation to reach its goals, ending with several findings and recommendations.

The use of electronic signatures as a form of identification is increasingly common, yet they have been shown to lack the dynamic features found in online signatures. In this study, handwritten signatures were scanned to produce electronically scanned signatures (ESS) which were then digitally altered to produce digitally constructed signatures (DCS). The ESS and DCS were presented back to participants to identify which were genuine. Only 1% of participants correctly identified all signatures, with a mean score of 57.6% identifications. The lack of self-recognition of ESS raises questions on their reliability and usefulness as means of personal identification.

Automatic pattern detection has become increasingly important for scholars in the humanities as the number of manuscripts that have been digitised has grown. Most of the state-of-the-art methods used for pattern detection depend on the availability of a large number of training samples, which are typically not available in the humanities as they involve tedious manual annotation by researchers (e.g. marking the location and size of words, drawings, seals and so on). This makes the applicability of such methods very limited within the field of manuscript research. We propose a learning-free approach based on a state-of-the-art Naïve Bayes Nearest-Neighbour classifier for the task of pattern detection in manuscript images. The method has already been successfully applied to an actual research question from South Asian studies about palm-leaf manuscripts. Furthermore, state-of-the-art results have been achieved on two extremely challenging datasets, namely the AMADI_LontarSet dataset of handwriting on palm leaves for word-spotting and the DocExplore dataset of medieval manuscripts for pattern detection. A performance analysis is provided as well in order to facilitate later comparisons by other researchers. Finally, an easy-to-use implementation of the proposed method is developed as a software tool and made freely available.

Brushstrokes are viewed as the artist’s “handwriting” in a painting. In many applications such as style learning and transfer, mimicking painting, and painting authentication, it is highly desired to quantitatively and accurately identify brushstroke characteristics from old masters’ pieces using computer programs. However, due to the nature of hundreds or thousands of intermingling brushstrokes in the painting, it still remains challenging. This article proposes an efficient algorithm for brush Stroke extraction based on a Deep neural network, i.e., DStroke. Compared to the state-of-the-art research, the main merit of the proposed DStroke is to automatically and rapidly extract brushstrokes from a painting without manual annotation, while accurately approximating the real brushstrokes with high reliability. Herein, recovering the faithful soft transitions between brushstrokes is often ignored by the other methods. In fact, the details of brushstrokes in a master piece of painting (e.g., shapes, colors, texture, overlaps) are highly desired by artists since they hold promise to enhance and extend the artists’ powers, just like microscopes extend biologists’ powers. To demonstrate the high efficiency of the proposed DStroke, we perform it on a set of real scans of paintings and a set of synthetic paintings, respectively. Experiments show that the proposed DStroke is noticeably faster and more accurate at identifying and extracting brushstrokes, outperforming the other methods.

Writing sequences play an important role in handwriting of Chinese characters. However, little is known regarding the integral brain patterns and network mechanisms of processing Chinese character writing sequences. The present study decoded brain patterns during observing Chinese characters in motion by using multi-voxel pattern analysis, meta-analytic decoding analysis, and extended unified structural equation model. We found that perception of Chinese character writing sequence recruited brain regions not only for general motor schema processing, i.e., the right inferior frontal gyrus, shifting, and inhibition functions, i.e., the right postcentral gyrus and bilateral pre-SMA/dACC, but also for sensorimotor functions specific for writing sequences. More importantly, these brain regions formed a cooperatively top-down brain network where information was transmitted from brain regions for general motor schema processing to those specific for writing sequences. These findings not only shed light on the neural mechanisms of Chinese character writing sequences, but also extend the hierarchical control model on motor schema processing.

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.

Handwriting Analysis is described as a scientific study for the analysis of handwriting. It is a way of interpreting and ability to learn from peculiarities in handwriting. Offline handwriting analysis is a traditional approach that cannot be used efficiently for analysis. Online handwriting analysis, on the other hand, can utilize various aspects like pressure on the pen, timestamp and other factors which help in improving the effectiveness of analysis. Learning disabilities are neurological processing problems which can hamper the learning of the children. Dysgraphia is a learning disability that mainly affects a child’s handwriting and motor skills. It is found in 10 to 30% of school-aged children. Dysgraphia can be diagnosed by therapists based on children’s handwriting samples and manual evaluation techniques. This method is lengthy and inaccurate. In this work, automatic identification methods for and classification of dysgraphia in children in the age group 7 to 12 is described. The method performs analyzing of the child’s writing dynamics via blueprints of the pressure the pen puts on paper with the pen’s movements and orientation with the use of a standardized digital writing pad and machine learning algorithms. It basically has two phases, the training phase, and testing phase. In the training phase, handwriting samples of known results are given to the system. Then the model is built using some classifier, Random forest or Support Vector Machine. Once the model is trained, then in testing phase this model is used for classification of unknown samples to predict whether the subject has dysgraphia or not. This is then used to check the accuracy of the designed system.

Arabic is a language that is spoken as the first or native language of more than 280 million people, most of whom live in the Middle East and North Africa. Apart from the Middle East and North Africa, Arabic is also familiar and often used in Indonesia because of the majority of Indonesia's population is Muslim and Arabic is the language of worship in Islam. The recognition of Arabic handwritten letters is one of the studies that has been done before, where the accuracy results obtained vary according to the research and methods used. This study aims to determine the accuracy resulting from the recognition of Arabic script handwriting patterns using a combination of the DCT(Discrete Cosine Transform) feature extraction method and the ANN Backpropagation classification method. The data used for this study were data from handwritten sources on A4 HVS paper using markers with categories of age from 7-13 years old and 18-23 years old with 15 respondents in each group and a total dataset image of 8400. Testing the best model model obtained on all images produces an accuracy of 80.79%, using the images of age range 17-23 years produces 87.27% accuracy, and the images of age range 7-13 produces an accuracy of 72.84%. Keywords: pengenalan pola, tulisan tangan, aksara, DCT, backpropagation

This paper proposes a technique for processing handwriting images. The algorithm used in this study is an improvement to the binarisation process. The enhancement focuses on correcting damaged lines that usually arise during the binarisation process, particularly, spurious holes, discontinuities, and eroded boundaries. The presented method uses a morphogical dilation operation in which a structural element is locally adapted using the information from a directional field. The adaptation process involves a new criterion for selecting orientation and shape of a structural element that combines directional field, a coherence measure, and a circular histogram. The field was computed using gradient-based approach, and a method based on a Hessian matrix. During experiments, our method was applied to the output of selected binarisation algorithms. The experiments were conducted on grayscale signature images (from the CEDAR database) and handwriting images (from the DIBCO database). The results of the algorithm were compared to the results of standard morphological operations (dilation, erosion, opening, and closing) and median filtering. The experiments show that the proposed method achieves significant accuracy improvement (8%–12% for Acc, 15%–32% for Acc2 measures), reduces the number of unwanted artefacts, and produces images with less distortion compared to those from standard approaches.

The etiology of Parkinson’s disease (PD) remains unclear. Symptoms usually appear after approximately 70% of dopamine-producing cells have stopped working normally. PD cannot be cured, but its symptoms can be managed to delay its progression. Evidence suggests that early diagnosis is important in establishing an effective pathway for management of symptoms. However, PD diagnosis is challenging, particularly in the early stages of the disease. In this paper, we present a method for early diagnosis of PD using patients’ handwriting samples. To improve performance, we combined multiple PD handwriting datasets and used deep transfer learning-based algorithms to overcome the challenge of high variability in the handwritten material. Our approach achieved excellent PD identification performance with 99.22% accuracy on illuminated task of combined HandPD, NewHandPD and Parkinson’s Drawing datasets, demonstrating the superiority of our approach over current state-of-the-art methods.

In-air handwriting is a contemporary human computer interaction (HCI) technique which enables users to write and communicate in free space in a simple and intuitive manner. Air-written characters exhibit wide variations depending upon different writing styles of users and their speed of articulation, which presents a great challenge towards effective recognition of linguistic characters. So, in this paper we have proposed an ensemble model for in-air handwriting recognition which is based on convolutional neural network (CNN) and a long short-term memory neural network (LSTM-NN). The method collaborates overall character trajectory appearance modeling and temporal trajectory feature modeling for efficient recognition of varied types of air-written characters. In contrast to two-dimensional handwriting, in-air handwriting generally involves writing of characters interlinked by a continuous stroke, which makes segregation of intended writing activity from insignificant connecting motions an intricate task. So, a two-stage statistical framework is incorporated in the system for automatic detection and extraction of relevant writing segments from air-written characters. Identification of writing events from a continuous stream of air-written data is accomplished by formulating a Markov Random Field (MRF) model, while the segmentation of writing events into meaningful handwriting segments and redundant parts is performed by implementation of a Mahalanobis distance (MD) classifier. The proposed approach is assessed on an air-written character dataset comprising of Assamese vowels, consonants and numerals. The experimental results connote that our hybrid network can assimilate more information from the air-writing patterns and hence offer better recognition performance than the state-of-the-art approaches.

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%.

The palm oil sector is just one of the popular sectors in Malaysia that offer one of the highest exports to Malaysia's economy. This study handles the research study of inventory control for Malaysian palm oil plantation. The existing inventory control cannot be running smoothly when the inventory are managed in different user interface systems which is by utilizing manual inventory control like Microsoft Excel and handwriting documents. Data collection was gathered with the targeted respondents, they are core members of the management team who have a great experience as well as understanding of their inventory system. In order to make PSS inventory control much easier as well as organized, an inventory application system is developed utilizing MS Excel Macro. The outcomes of this research study are a computerized supply control system that beneficial, crucial, and also much better substitute for a hand-operated administration system and quick handling.

Existing handwriting recognition solution on mobile app provides off premise service which means the handwriting is processed in overseas servers. Data sent to abroad servers are not under our control and could be possibly mishandled or misused. As recognizing handwriting is a complex problem, deep learning is needed. This research has the objective of developing an on premise Indonesian handwriting recognition using open source deep learning solution. Comparison of various deep learning solution to be used in the development are done. The deep learning solution will be used to build architectures. Various database format are also compared to decide which format is suitable to gather Indonesian handwriting database. The gathered Indonesian handwriting database and built architectures are used for experiments which consists of number of Convolutional Neural Network (CNN) layers, rotation and noise data augmentation, and Gated Recurrent Unit (GRU) vs Long Short Term Memory (LSTM). Experiment results shows that rotation data augmentation is the parameter to be change to improve word accuracy and Character Error Rate (CER). The improvement is 64.8% and 23.2% to 69.6% and 20.6% respectively.