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Optical Character Recognition OCR is an essential part of many real-world applications such as digital archiving, automatic number plate recognition, handle cheques, etc. However, developing an OCR for printed Arabic text is still a challenging and open research field due to the special characteristics of Arabic cursive script. In this paper, we propose a segmentation-based, omnifont, open-vocabulary OCR for printed Arabic text. The proposed approach doesn’t require an explicit font type recognition stage. It uses an explicit, indirect character segmentation method. The presented segmentation method is baseline dependent and employs a hybrid, three-steps character segmentation algorithm to handle the problem of character overlapping. Besides, it uses a set of topological features that are designed and generalized to make the segmentation approach font independent. The segmented characters are fed as an input to a convolutional neural network for feature extraction and recognition. The APTID-MF data set has been used for testing and evaluation. The average accuracy of the proposed segmentation stage is 95%, while the average accuracy of the recognition stage is 99.97%. The whole approach achieves an average accuracy of 95% without using font-type recognition or any post-processing techniques.

Characters segmentation is a necessity and the most critical stage in Arabic OCR system. It has attracted the interest of a wide range of researchers. However, the nature of the Arabic cursive script poses extra challenges that need further investigation. Therefore, having a reliable and efficient Arabic OCR system that is independent of font variations is highly required. In this paper, an indirect, font-in dependent word and character segmentation algorithm for printed Arabic text investigated. The proposed algorithm takes a binary line image as an input and produces a set of binary images consisting of one character or ligature as an output. The segmentation performed at two levels: a word segmentation performed in the first level, by employing a vertical projection at the input line image along with using Interquartile Range (IQR) method to differentiate between word gaps and within word gaps. A projection profile method used as a second level of segmentation along with a set of statistical and topological features, which are font-independent, to identify the correct segmentation points from all potential points. The APTI dataset used to test the proposed algorithm with a variety of font type, size, and style. The algorithm experimented on 1800 lines (approximately 24,816 words) with an average accuracy of 97.7% for words segmentation and 97.51% for characters segmentation.

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.

Neste artigo, insistimos na defesa de uma proposta de conciliação teórica-metodológica entre duas teorias linguísticas preocupadas com a mudança linguística – a Gramaticalização e a Sociolinguística –, destacando o pioneirismo da Linguística brasileira na elaboração dessa proposta, que passou a ser conhecida como “Sociofuncionalismo”. Para tratar dos pontos de convergência e divergência entre Teoria da Gramaticalização e Teoria da Variação e Mudança Linguística, partimos dos trabalhos de Naro e Braga (2000) e de Görski e Tavares (2013, 200-?), por terem sido os primeiros a responderem questões relevantes que levariam ao trabalho de interface entre os dois modelos teóricos. Ilustramos essa proposta de conciliação com casos de perífrases verbais de aspecto cursivo que, resultantes de gramaticalização, podem ser abordadas sob perspectiva sociofuncionalista. Primeiramente testamos dois critérios de gramaticalização – frequência de uso e parâmetros de auxiliaridade – e depois mostramos os contextos de variação entre as perífrases, provando, assim, a pertinência da proposta.

According to statistics, there are 422 million speakers of the Arabic language. Islam is the second-largest religion in the world, and its followers constitute approximately 25% of the world’s population. Since the Holy Quran is in Arabic, nearly all Muslims understand the Arabic language per some analytical information. Many countries have Arabic as their native and official language as well. In recent years, the number of internet users speaking the Arabic language has been increased, but there is very little work on it due to some complications. It is challenging to build a robust recognition system (RS) for cursive nature languages such as Arabic. These challenges become more complex if there are variations in text size, fonts, colors, orientation, lighting conditions, noise within a dataset, etc. To deal with them, deep learning models show noticeable results on data modeling and can handle large datasets. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) can select good features and follow the sequential data learning technique. These two neural networks offer impressive results in many research areas such as text recognition, voice recognition, several tasks of Natural Language Processing (NLP), and others. This paper presents a CNN-RNN model with an attention mechanism for Arabic image text recognition. The model takes an input image and generates feature sequences through a CNN. These sequences are transferred to a bidirectional RNN to obtain feature sequences in order. The bidirectional RNN can miss some preprocessing of text segmentation. Therefore, a bidirectional RNN with an attention mechanism is used to generate output, enabling the model to select relevant information from the feature sequences. An attention mechanism implements end-to-end training through a standard backpropagation algorithm.

Urdu is one of the most popular languages in the world. It is a Persianized standard register of the Hindi language with considerable and valuable literature. While digital libraries are constantly replacing conventional libraries, a vast amount of Urdu literature is still handwritten. Digitizing this handwritten literature is essential to preserve it and make it more accessible. Nevertheless, the scarcity of Urdu Optical Character Recognition (OCR) research limits a digital library's scope to a manual document search. The limited research work in this area is mainly due to the complexity of Urdu Script. Unlike the English language, the Urdu writing style is cursive, bidirectional, and character shapes and sizes highly vary depending on their position. Holistic word recognition is found to be a better solution among many other text segmentation techniques as it takes the complete word into account instead of segmenting it explicitly or implicitly. For this project, the data of five different Urdu words were collected for training and testing a convolutional neural network and 96% recognition accuracy was achieved.

Multi-oriented handwritten documents require additional preprocessing for segmentation and subsequent phases to work accurately in handwritten recognition systems. Skew correction is one such additional phase. Appearance of skew in multi-oriented Indian language based handwritten document is higher due to the presence of cursive nature. In the current work, we utilise a salient feature present in Indian scripts called $m\bar {a}$ tr $\bar {a}$ (also known as headline), extract a group of eligible pixels, and employ linear curve fitting for detecting and correcting skew in handwritten words. The proposed method is capable of correcting skew in four distinct Indian languages, viz. Bangla, Hindi, Marathi, and Punjabi. It is capable of efficiently handling skewed word images to an extent of ± 55∘ and delivers precise result even when the $m\bar {a}$ tr $\bar {a}$ is mostly absent or discontinuous.

An optical character recognition (OCR) system may be the solution to data entry problems for saving the printed document as a soft copy of them. Therefore, OCR systems are being developed for all languages, and Kurdish is no exception. Kurdish is one of the languages that present special challenges to OCR. The main challenge of Kurdish is that it is mostly cursive. Therefore, a segmentation process must be able to specify the beginning and end of the characters. This step is important for character recognition. This paper presents an algorithm for Kurdish character segmentation. The proposed algorithm uses the projection-based approach concepts to separate lines, words, and characters. The algorithm works through the vertical projection of a word and then identifies the splitting areas of the word characters. Then, a post-processing stage is used to handle the over-segmentation problems that occur in the initial segmentation stage. The proposed method is tested using a data set consisting of images of texts that vary in font size, type, and style of more than 63,000 characters. The experiments show that the proposed algorithm can segment Kurdish words with an average accuracy of 98.6%.

Recognition of handwritten digits is one of the most important and challenging issues in recent decades in the field of computer science. Its cursive nature, the right to left writing styles of words and characters as well as various digits shapes have imposed curiosity among numerous researchers to impose a lot of efforts on the recognition of handwritten Farsi numbers. In order to improve the recognition accuracy of Farsi handwritten digit recognition, the pragmatic CBWME network structure model based on convolution bagging weighted majority ensemble learning is developed by integrating the convolution neural network (CNN) and bagging weighted majority ensemble learning. For base classifiers, we applied the VGG16, ResNet18, and Xception architectures and explored the bagging weighted majority ensemble learning in combining the base classifiers results, which are later used in identifying Farsi handwritten digits. The performance of the CNN models (VGG16, ResNet18, and Xception) and CBWME model was evaluated by comparing their results. From the experimental result analysis, it was observed that the proposed CBWME model achieved the best average recognition accuracy (97.65%), followed by the Xception model (95.9%), ResNet18 model (93.75%), and VGG16 model (90.26%) in HODA dataset. The accuracy orders were the same as in IFHCDB and CENPARMI datasets. The CSE model attained the best result with rate of (99.876%) compared with the other studies.

The article provides a retrospective analysis of the legal regulations that governed the primary protonotarial activity in the northern lands of Russia. Some provisions of the Russkaya Pravda, as a basic act of ancient Russia, as well as documents of the Galicia-Volyn principality are considered: Charter of Prince Ivan Rostislavovich (1134), "Manuscript" of Prince Vladimir Vasilyevich (1287), Charter of Mstislav Romanovich (1289). A professional review of scientific literature and legal acts is presented, which mentions proto-registration activities and the compilation of individual acts of a private legal nature by the so-called areal clerks (or cursive writers), which were common in the lands of northern Russia, over which Kiev lost its control. The analysis covered the periods before and after the entry into force of the Code of 1649 until the end of the era of cursive writers just before the beginning of the 18th century. The questions of the symbolism of law in the execution of transactions with land, transactions with property, transactions where the subject were living people were touched upon. The formation of the notary profession as a non-state free and self-governing professional institution is traced. The issues of the activities of areal clerks are considered in detail. Specific examples of the documents they draw up are given, as well as materials of law enforcement practice. Expansion on the part of the state is illustrated in order to take control of the activities of the first notaries, to curtail and further eliminate their independence and independence from the state and its bureaucracy. The works of the XIX - early years of the XX century are widely used, which contain the provisions of ancient legal acts and documents that are inaccessible to modern researchers.

In the area of machine learning, different techniques are used to train machines and perform different tasks like computer vision, data analysis, natural language processing, and speech recognition. Computer vision is one of the main branches where machine learning and deep learning techniques are being applied. Optical character recognition (OCR) is the ability of a machine to recognize the character of a language. Pashto is one of the most ancient and historical languages of the world, spoken in Afghanistan and Pakistan. OCR application has been developed for various cursive languages like Urdu, Chinese, and Japanese, but very little work is done for the recognition of the Pashto language. When it comes to handwritten character recognition, it becomes more difficult for OCR to recognize the characters as every handwritten character’s shape is influenced by the writer’s hand motion dynamics. The reason for the lack of research in Pashto handwritten character data as compared to other languages is because there is no benchmark dataset available for experimental purposes. This study focuses on the creation of such a dataset, and then for the evaluation purpose, a machine is trained to correctly recognize unseen Pashto handwritten characters. To achieve this objective, a dataset of 43000 images was created. Three Feed Forward Neural Network models with backpropagation algorithm using different Rectified Linear Unit (ReLU) layer configurations (Model 1 with 1-ReLU Layer, Model 2 with 2-ReLU layers, and Model 3 with 3-ReLU Layers) were trained and tested with this dataset. The simulation shows that Model 1 achieved accuracy up to 87.6% on unseen data while Model 2 achieved an accuracy of 81.60% and 3% accuracy, respectively. Similarly, loss (cross-entropy) was the lowest for Model 1 with 0.15 and 3.17 for training and testing, followed by Model 2 with 0.7 and 4.2 for training and testing, while Model 3 was the last with loss values of 6.4 and 3.69. The precision, recall, and f-measure values of Model 1 were better than those of both Model 2 and Model 3. Based on results, Model 1 (with 1 ReLU activation layer) is found to be the most efficient as compared to the other two models in terms of accuracy to recognize Pashto handwritten characters.

The remarkable growth of texts both in online and offline is becoming a challenging issue which need exploration for further research. Diversities of regional and cultural changes have produced diverse languages as a source of communication. Variations of styles are existing for handwritten texts which is due to varying writing styles. The research area of text recognition is matured which has increased a number of directions in the area of research. A detail report of the existing literature is needed which can help practitioners and researchers to use the existing evidence and provide new solutions for identification of cursive languages and to optimize the ability of recognition for cursive text. For facilitating the researchers and practitioners by providing in-depth analysis of the existing literature, the proposed study provide a detail report through which researchers can get benefit of the literature and devise new solutions. This study is based on searching various popular libraries for identifying relevant materials associated with the proposed study.

Decomposition of a word into a set of appropriate pseudo-characters is a challenging task in case of a cursive script like Bangla. Segmentation-free approach bypasses the decomposition problem entirely and treats the handwritten word as an individual entity. From the literature, we found that the accuracy of handwritten Bangla cursive word recognition using segmentation-free approach is relatively low (accuracy hovers between 80% and 90%). In the current work, we aim to provide a threefold study on this particular domain. Firstly, we extract different statistical feature sets from word images and use five different off-the-shelf classifiers to delineate their performance. Then, we employ five different CNN-TL architectures, namely AlexNet, VGG-16, VGG-19, ResNet50, and GoogleNet, to understand how they perform on holistic Bangla words. Finally, we use a seven-layer FCN architecture and provide a comparison of results with all the aforementioned experimentations. We achieved an accuracy of 98.86% with ResNet50, which is nearly 19% improvement when compared with other recent state-of-the-art methodologies.

Signboard detection and recognition is an important task in automated context-aware marketing. Recently many scripting languages like Latin, Japanese, and Chinese have been effectively detected by several machine learning algorithms. As compared to other languages, outdoor Urdu text needs further attention in detection and recognition due to its cursive nature. Urdu detection and recognition are also difficult due to a wide variety of illuminations, low resolution, inconsistent font styles, color, and backgrounds. To overcome the deficiency of Urdu text detection from the outdoor environment, we have proposed a new Urdu-text signboard dataset with 467 ligature categories, containing a 30 + K images for recognition and 700 base images with annotation are created for detection. We also propose a methodology, that consists of 3-phases. In first phase text regions containing Urdu ligatures from shop-signboard images are detected by a faster regional convolutional neural network (FasterRCNN) using pre-trained CNNs like Alexnet and Vgg16. In the second phase detected regions from the first phase are clustered to identify unique ligatures in a dataset. Lastly in the third phase, all detected regions are recognized by 18-layer convolutional neural network trained model. The proposed system has successfully achieved the precision and recall of 87% and 96% respectively using vgg16 model for detection. For the classification of ligatures, a recognition rate of 97.50% is achieved. Recognition of ligatures was also evaluated using bilingual evaluation understudy (BLEU), and achieved an encouraging score of 0.96 on the newly developed Urdu-Signboard dataset.

Optical character recognition, known as OCR, has been widely used due to high demand of different technologies. Currently, most existing OCR systems have been focused on Latin languages. In recent studies, OCR systems for non-Latin texts involving cursive style have also been introduced despite posing some challenges. In this study, the authors propose an OCR system based on long short-term memory neural networks for the Persian language. The authors also investigate the effects of variations of parameters, involved in this approach. The proposed OCR system solves false recognition of sub-word ‘LA’ and ‘LA’. Moreover, the authors present a preprocessing algorithm to remove ‘justification’ using image processing. A new comprehensive collated data set is introduced, comprising five million images with eight popular Persian fonts and in ten various font sizes. The proposed evaluations show that the accuracy of the proposed OCR is increased by 2%, compared to the existing Persian OCR system. The experimental results indicated that the proposed system has average accuracy of 99.69% at the letter level. The proposed system has an accuracy of 98.1% for ‘zero-width non-breaking space’ and 98.64% for ‘LA’ at the word level.

This study focuses on cursive text recognition appearing in videos, using a complete framework of deep neural networks. While mature video optical character recognition systems (V-OCRs) are available for text in non-cursive scripts, recognition of cursive scripts is marked by many challenges. These include complex and overlapping ligatures, context-dependent shape variations and presence of a large number of dots and diacritics. The authors present an analytical technique for recognition of cursive caption text that relies on a combination of convolutional and recurrent neural networks trained in an end-to-end framework. Text lines extracted from video frames are preprocessed to segment the background and are fed to a convolutional neural network for feature extraction. The extracted feature sequences are fed to different variants of bi-directional recurrent neural networks along with the ground truth transcription to learn sequence-to-sequence mapping. Finally, a connectionist temporal classification layer is employed to produce the final transcription. Experiments on a data set of more than 40,000 text lines from 11,192 video frames of various News channel videos reported an overall character recognition rate of 97.63%. The proposed work employs Urdu text as a case study but the findings can be generalised to other cursive scripts as well.

Motivation : The problem of handwritten text recognition is vastly studied since last few decades. Many innovative ideas have been developed, where state-of-the-art accuracy is achieved for the English, Chinese or Indian scripts.The recent developments for the cursive scripts such as Arabic and Urdu handwritten text recognition have achieved remarkable accuracy. However, for the Sindhi script, existing systems have not shown significant results and the problem is still an open challenge. Several challenges such as variations in writing styles, joined text, ligature overlapping, and others associated to the handwritten Sindhi text make the problem more complex. Objectives: In this study, a deep residual network with shortcut connections and summation fusion method using convolutional neural network (CNN) is proposed for automatic feature extraction and classification of handwritten Sindhi characters. Method: To increase the powerful feature representation ability of the network, the features of the convolutional layers in the residual block are fused together and combined with the output of the previous residual block. The proposed network is trained on a custom developed handwritten Sindhi character dataset. To tackle the problem of small data, a data augmentation with rotation, flipping and image enhancement techniques have been used. Findings: The experimental results show that the proposed model outperforms than the best results previously published for the handwritten Sindhi character recognition. Novelty: This is the first research that proposes deep residual network with summation fusion for the Sindhi handwritten text recognition. Keywords: Handwritten Sindhi character recognition; Sindhi text recognition; cursive text recognition; deep learning; ResNet; convolutional neural network

Chinese is a widely used language in the world. Chinese cursive script is one of the most distinctive calligraphy art and traditional cultures of China. However, for its connected writing, there is a lack of research on text recognition for cursive images. Here, the authors construct a small cursive image dataset named as Chinese Cursive and there are 523 images in this dataset. It contains continuous strokes text, recognises difficulty etc. Each cursive character is corresponded to a label. The authors proposed a cursive detection method named as SE-seglink for the dataset. The SE-seglink further enhances the image feature extraction. Compared to the existing methods, the SE-seglink performs better in recognising cursive scripts and improves the precision of text detection in cursive images. After multiple sets of comparative experiments, the effectiveness of the SE-seglink method was evaluated by the experiment on the benchmark image dataset ICDAR2015.

Building a robust Optical Character Recognition (OCR) system for languages, such as Arabic with cursive scripts, has always been challenging. These challenges increase if the text contains diacritics of different sizes for characters and words. Apart from the complexity of the used font, these challenges must be addressed in recognizing the text of the Holy Quran. To solve these challenges, the OCR system would have to undergo different phases. Each problem would have to be addressed using different approaches, thus, researchers are studying these challenges and proposing various solutions. This has motivate this study to review Arabic OCR dataset because the dataset plays a major role in determining the nature of the OCR systems. State-of-the-art approaches in segmentation and recognition are discovered with the implementation of Recurrent Neural Networks (Long Short-Term Memory-LSTM and Gated Recurrent Unit-GRU) with the use of the Connectionist Temporal Classification (CTC). This also includes deep learning model and implementation of GRU in the Arabic domain. This paper has contribute in profiling the Arabic text recognition dataset thus determining the nature of OCR system developed and has identified research direction in building Arabic text recognition dataset.

In this paper, we propose integrated adaptive sensitivity and robustness terms for the cost function of a convolutional neural network (CNN). The sensitivity term considers the slight variations and high frequency components of the input image samples. It distinguishes between images that look similar but belong to different classes. This regularizer is designed to enhance the between-class distance which is a biological definition for the simple cells of the visual system. On the other hand, the robustness term is used to develop a more stable CNN structure against disturbances and perturbations. The robust term provides better within-class features because it recognizes images that look different but are actually from the same class. The robust term symbolizes the complex cell characteristics of the visual system. The coefficients of the sensitivity and robustness regularization terms are adaptively tuned along with the network parameters using gradient descent. Two optimizers are assigned to tune the parameters: one for tuning the model parameters and the other one to adjust the sensitivity and robustness coefficients. This approach is applied to Korean traditional documents for character classification. The results show better within- and between-class classification ability for highly complex character styles with imbalanced number of samples.

There has been little research concerning Japanese calligraphy in Western art history. Even in Japan the calligraphy of syllabic scripts kana has been actively studied only during the last ten years. The complexity of the study lies in the fact that the works of kana calligraphy are usually written in old Japanese, combined with cursive Chinese characters (‘mixed writing’), which requires special training even from a Japanese researcher. Since the calligraphy of kana is tightly connected with the poetry of waka and haiku, within this paper we analyze it in its relation to the history of traditional Japanese poetry. The objective of this study is to define the specific nature of kana calligraphy, which reflects a special ‘poetic sentiment’ inherited from waka and haiku. In this research we compare kana with Chinese calligraphy characters and discover that kana calligraphy is fluid in principal, matching the aesthetics of waka, which has a flowing of meanings, as a rule. We also analyze the later influence of haiku on kana calligraphy and conclude that haiku partly reverts the latter to kana Chinese calligraphy characters (where scripts can be written separately). In this paper we analyze the works of the most famous Japanese calligraphers who specialized in kana: Ono-no Michikaze (802–853), Fujiwara-no Yukinari (966–1028), Goho Hibino (1901–1985). We conclude that a distinctive feature of kana calligraphy in waka is sensual beauty while calligraphy in haiku is more restrained and simple. The results of the research are considered to be important not only as a theoretical basis for further investigations of kana, but they also present the grounds for a general reconsidering of the European art of writing as well.

This study compares the characteristics of the aesthetic experience of different cognitive styles in calligraphy style. The study used a cursive script and running script as experimental materials and the EyeLink 1000 Plus eye tracker to record eye movements while viewing calligraphy. The results showed that, in the overall analysis, there were differences in the field cognition style in total fixation counts, saccade amplitude, and saccade counts and differences in the calligraphic style in total fixation counts and saccade counts. Further local analysis found significant differences in the field cognitive style in mean pupil diameter, fixation counts, and regression in count, and that there were differences in fixation counts and regression in count in the calligraphic style, as well as interactions with the area of interest. The results indicate that the field cognitive style is characterized by different aesthetic experiences in calligraphy appreciation and that there are aesthetic preferences in calligraphy style.

Recognition of cursive handwritten Arabic text is a difficult problem because of context-sensitive character shapes, the non-uniform spacing between words and within a word, diverse placements of dots, and diacritics, and very low inter-class variation among individual classes. In this paper, we review and investigate different deep learning architectures and modeling choices for Arabic handwriting recognition. Further, we address the problem that imbalanced data sets present to deep learning systems. In order to address this issue, we are presenting a novel adaptive data-augmentation algorithm to promote class diversity. This algorithm assigns a weight to each word in the database lexicon. This weight is calculated based on the average probability of each class in a word. Experimental results on the IFN/ENIT and AHDB databases have shown that our presented approach yields state-of-the-art results.

Urdu text is a cursive script and belongs to a non-Latin family of other cursive scripts like Arabic, Chinese, and Hindi. Urdu text poses a challenge for detection/localization from natural scene images, and consequently recognition of individual ligatures in scene images. In this paper, a methodology is proposed that covers detection, orientation prediction, and recognition of Urdu ligatures in outdoor images. As a first step, the custom FasterRCNN algorithm has been used in conjunction with well-known CNNs like Squeezenet, Googlenet, Resnet18, and Resnet50 for detection and localization purposes for images of size $320\times 240$ pixels. For ligature Orientation prediction, a custom Regression Residual Neural Network (RRNN) is trained/tested on datasets containing randomly oriented ligatures. Recognition of ligatures was done using Two Stream Deep Neural Network (TSDNN). In our experiments, five-set of datasets, containing 4.2K and 51K Urdu-text-embedded synthetic images were generated using the CLE annotation text to evaluate different tasks of detection, orientation prediction, and recognition of ligatures. These synthetic images contain 132, and 1600 unique ligatures corresponding to 4.2K and 51K images respectively, with 32 variations of each ligature (4-backgrounds and font 8-color variations). Also, 1094 real-world images containing more than 12k Urdu characters were used for TSDNN’s evaluation. Finally, all four detectors were evaluated and used to compare them for their ability to detect/localize Urdu-text using average-precision (AP). Resnet50 features based FasterRCNN was found to be the winner detector with AP of.98. While Squeeznet, Googlenet, Resnet18 based detectors had testing AP of.65,.88, and.87 respectively. RRNN achieved and accuracy of 79% and 99% for 4k and 51K images respectively. Similarly, for characters classification in ligatures, TSDNN attained a partial sequence recognition rate of 94.90% and 95.20% for 4k and 51K images respectively. Similarly, a partial sequence recognition rate of 76.60% attained for real world-images.

One of the most challenging module in any handwritten character recognition system is separating individual characters from the document. Better character recognition rate is highly influenced by how much accuracy, character segmentation phase, has achieved. As the input data set is contains documents written in MODI script, and since MODI script characters are written in cursive manner, obtaining individual characters from the text becomes extremely complicated. Moreover, there is no separation between two adjacent words. Although now-a-days, Marathi language uses Devnagari script, several years back MODI Script was used for writing documents in Marathi language. This work presented in this paper tackles segmentation of individual characters from ancient handwritten MODI Script documents. Vertical Projection Profile (VPP) method can segment characters from a given line accurately only when two adjacent characters are separated by a zero-pixel column. Since characters of one line in this script are written without lifting hand, output of VPP does not suffice. This paper presents a novel approach for isolating individual characters from the line obtained using authors’ previous research work which uses dual thresholding criterion to minimize the character segmentation error. The techniques used in this paper are straightforward and reasonably economical in terms of execution time efficiency. The results obtained are quite satisfactory.