Handwritten Arabic Touching and overlapping Characters Separation Methods

Text line segmentation is a necessary preliminary step before most text transcription algorithms are applied. The leading deep learning networks used in this context (ARU-Net, dhSegment, and Doc-UFCN) are based on the U-Net architecture. They are efficient, but fall under the same concept, requiring a post-processing step to perform instance (e.g., text line) segmentation. In the present work, we test the advantages of Mask-RCNN, which is designed to perform instance segmentation directly. This work is the first to directly compare Mask-RCNN- and U-Net-based networks on text segmentation of historical documents, showing the superiority of the former over the latter. Three studies were conducted, one comparing these networks on different historical databases, another comparing Mask-RCNN with Doc-UFCN on a private historical database, and a third comparing the handwritten text recognition (HTR) performance of the tested networks. The results showed that Mask-RCNN outperformed ARU-Net, dhSegment, and Doc-UFCN using relevant line segmentation metrics, that performance evaluation should not focus on the raw masks generated by the networks, that a light mask processing is an efficient and simple solution to improve evaluation, and that Mask-RCNN leads to better HTR performance.

The purpose of this survey is to provide a comprehensive overview of recent advancements in text line segmentation and baseline detection techniques within the analysis of historical document images. Text line extraction is an essential step in the historical documents image analysis pipeline, as its results significantly impact the accuracy of subsequent processes, such as handwritten text recognition (HTR). Through a multi-stage procedure, we carefully selected 49 peer-reviewed studies published since 2019. Based on careful analysis of these studies, we summarize the information of the existing datasets, describe and categorize different methods, and summarize evaluation protocols. In addition, we compare the results of various methods on benchmark datasets. Finally, we highlight the gaps and suggest directions for future research. We believe that this comprehensive survey will be of great assistance to researchers working in the field of historical document image analysis, as it offers critical insights into the latest advancements and developments, providing a foundation for future research.

Segmentation of text lines is one of the important steps in the Optical Character Recognition system. Text Line Segmentation is pre-processing step of word and character segmentation. Text Line Segmentation can be viewed simple for printing documents which contains distinct spaces between the lines. And it is more complex for the documents where text lines are overlap, touch, curvilinear and variation of space between text lines like in Telugu scripts and skewed documents. The main objective of this project is to investigate different text line segmentation algorithms like Projection Profiles, Run length smearing and Adaptive Run length smearing on low quality documents. These methods are experimented and compare their accuracy and results.

Text-line segmentation is one of the key factors which affect the performance of handwriting recognition system. Therefore, to make recognition systems more effective and accurate, segmentation of touching text-lines is an important task. One of the problems making this task crucial is the presence of touching components (TCs) representing connections between word letters of consecutive text-lines or those of words of the same text-line. The proposed method aims to segment TCs. It is mainly based on two steps: (1) finding for a localized TC a similar model, stored in a dictionary with its correct segmentation, using shape context descriptor and an interpolation function: the thin plate spline transformation, (2) segmenting the TC based on central point of the found similar model parts. TCs are assumed to be already extracted from Arabic manuscript images. Experiments are carried on a common TC database, using two metrics: Manhattan and Euclidean distances. Obtained results outperform the state of the art, considering the different types, variability and complexity of the TCs data set, and show the effectiveness of the proposed TC segmentation method.

In this work, we propose a character segmentation system from ancient palm leaf manuscripts written in ancient Arya Ezhuthu(which was popularized as script to write Malayalam from 17 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> century). The automatic recognition of handwritten text from digital images of palm leaves dating back to 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> century BC. This can enable context based searching in large volumes of digital document. The subject about document image understanding is to extract and classify individual data meaningfully from palm leaf manuscript. We propose an automatic approach to detecting and identifying the characters. The proposed approach can improve the readability of the document. Text line segmentation is then applied and the characters are extracted.

Segmentation is a very critical stage in the character recognition process as the performance of any character recognition system depends heavily on the accuracy of segmentation. Although segmentation is a well-researched area, segmentation of handwritten text is still difficult owing to several factors like skewed and overlapping lines, the presence of touching, broken and degraded characters, and variations in writing styles. Therefore, researchers in this area are working continuously to develop new techniques for the efficient segmentation and recognition of characters. In the character recognition process, segmentation can be implemented at the line, word, and character level. Text line segmentation is the first step in the text/character recognition process. The line segmentation methods used in the character recognition of handwritten documents are presented in this paper. The various levels of segmentation which include line, word, and character segmentation are discussed with a focus on line segmentation.KeywordsSegmentationOCRPattern recognitionLine segmentationWord segmentationCharacter segmentationTouching lines segmentationOverlapping lines segmentation

A text recognition system for Japanese documents is described. The system is composed of a personal computer, which is used as a controller; an image scanner; and a recognition unit. There are four processing stages: text-line segmentation, character segmentation, character recognition, and postprocessing using the Japanese dictionary. Experimental results of the tests for Japanese handwritten technical reports are presented.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The application of deep neural network technologies to the problem of handwriting recognition in pre-reform Russian is considered. The initial data used are scanned JPG images of historical documents from the 19 th century, in particular containing various noises and interference, which complicates the work of the recognition algorithm. Text recognition is performed in three stages: noise removal, segmentation (highlighting) of text lines in the image, since the input data for the deep neural network are precisely the lines, and then recognition of the text of the highlighted lines using the pre-trained Tesseract OCR model, which performs electronic translation of images of handwritten or printed text into text data. The model used is a convolutional recurrent neural network; the model is a combination of a convolutional neural network for extracting local features from an image and a recurrent neural network represented by two layers of bidirectional LSTM networks for processing the sequence. Using this model allows for reliable recognition of handwritten text.

The object of the research is the line segmentation task. To recognize the handwritten text from the documents in image format offline handwriting recognition technology is used. The text recognizer module accepts input as separate lines, so one of the important preprocessing steps is the detection and splitting of all handwritten text into distinct lines. In this paper, the handwritten text line segmentation task, its requirements, problems, and challenges are examined. Two main approaches for this task that are used in modern recognition systems are reviewed. These approaches are statistical projection-based methods and neural-based methods. Multiple works and research papers for each type of approach are reviewed analyzing their strengths and weaknesses considering the described tasks, constraints, and input data peculiarities. Overall acquired results are formed in a single table for comparison. Based on the latest works that utilize deep neural networks the new possibilities of using these methods in recognition systems are described that were unavailable with traditional statistical segmentation approaches. The constructive conclusions are made based on the review, describing the main pros and cons of these two approaches for the line segmentation task. These results can be further used for the correct selection of suitable methods in handwriting recognition systems to improve their performance and quality, and for further research in this area.

Optical Character Recognition (OCR) and Handwritten Text Recognition (HTR) are technologies that enable text recognition. The difference between OCR and HTR is one designed specifically for digital text and one designed for handwritten text. There are already various implementations of OCR and HTR online. However, such systems do not guarantee the systems are in premises. To solve this problem, the OCR and HTR system must be built from the scratch. The purpose of this research is to improve the recognition by separating the text whether it is a handwritten or a printed text, which will later be forwarded into the appropriate recognition system. An application program interface (API) was also created in order to finalize the classification system into real world usage. In this research, the classification system being developed using convolutional neural network (CNN) method. To be able to reach the highest accuracy of the classification system, the experimentation and improvement about hyperparameters, dataset format, data augmentation and analysis on 3 CNN architectures were conducted. In the end of this research, there are 2 architectures in a tight competition, one is VGG-16 with 90.63% accuracy and one is AlexNet with 90.17% accuracy on ideal data testing. However, AlexNet is chosen as the winner after the testing with real data.

The study aims to comprehensively analyse existing methods and technologies for handwritten text recognition to justify the optimal approach to developing an effective recognition system for mobile applications.Methodology.The work uses a systematic literature review method to analyse scientific publications and technical documentation in handwritten text recognition.A comparative analysis was used to evaluate the advantages and disadvantages of different approaches, from traditional methods (pattern recognition, feature extraction) to modern solutions based on machine learning (k-NN, SVM) and deep neural networks (CNN, RNN).An analytical method was used to justify the optimal solution's choice theoretically, and a synthetic method was used to form a hybrid approach.Results.A detailed analysis of eight categories of handwritten text recognition methods was conducted.It was found that traditional methods have limited effectiveness due to their low adaptability to variations in handwriting.Machine learning algorithms show better results but depend on the quality of pre-processing.Deep learning methods demonstrate the highest efficiency due to automatic feature extraction and modelling of complex dependencies.A combined CNN + RNN + CTC approach is justified as the optimal solution for handwritten text recognition.Scientific novelty.A comprehensive systematisation of handwritten text recognition methods has been carried out, considering the specifics of mobile platforms.The advantages of a hybrid architecture combining convolutional and recurrent neural networks with the CTC function for recognition tasks without prior segmentation have been scientifically substantiated.A methodological approach to selecting optimal algorithms based on criteria of accuracy, speed, and adaptability to mobile constraints has been developed.Practical significance.The research results provide a theoretical basis for developing practical mobile applications for handwritten text recognition.The proposed approach ensures high recognition accuracy, adaptability to different handwriting styles, and optimisation for mobile devices.Developers can use the systematisation of methods to make informed decisions about technology selection.The research opens up prospects for creating Ukrainian-language recognition systems and further developing the field of digital processing of handwritten documents.

Most handwriting recognition systems need a mechanism for handling classification errors. These errors are typically caused by the large shape variability of the handwriting produced by different writers and by the segmentation errors, which occur when the word recognition process is performed by extracting and classifying single characters. In this paper, in order to reduce the segmentation errors, we propose a hierarchical recognition system composed of two classification modules. The first one discriminates isolated characters from cursive fragments using specifically devised features. The second one is an OCR engine that receives as input only those samples classified as isolated characters in the previous module. The whole system works like a highly reliable OCR that rejects most of the cursive fragments avoiding their incorrect classification. The experimental results confirmed the effectiveness of the proposed system.

Handwriting recognition is a process of recognizing handwritten text on a paper in the case of offline handwriting recognition and on a tablet in the case of online handwriting recognition and converting it into an editable text. In this chapter, the authors focus on offline handwriting recognition, which means that recognition system accepts a scanned handwritten page as an input and outputs an editable recognized text. Handwriting recognition has been an active research area for more than four decades, but some of the major problems still remained unsolved. Many techniques, including the machine learning techniques, have been used to improve the accuracy. This chapter focuses on describing the problems of handwriting recognition and presents the solutions using machine learning techniques for solving major problems in handwriting recognition. The chapter also reviews and presents the state of the art techniques with results and future research for improving handwriting recognition.

Results of document scanning are digital images. Image processing is considered as bidimensional signal processing. Such images are the input of document analysis and recognition systems. Information extraction is one objective of document analysis and recognition systems. Textline extraction is a crucial step of such systems because its output is considered as the input of the recognition step. Most segmentation methods take as input binary images, which explains that binarization methods can affect segmentation results. We study in this paper how does the choice of binarization minimally affects the results of text-line segmentation methods? Several evaluation metrics are used for the comparison between segmentation results. The proposed approach is tested using the benchmarking databases IAM (about 556 images) and IAM historical (about 60 images). The results show that binarization affects the detection rate (DR) and the recognition accuracy (RA) metrics for segmentation evaluation.

The adaptation module is a Radial Basis Function Neural Network (RBF-NN) that can be connected to the output of any recognition system and its aim is to examine the output of the writer-independent system and produce a more correct output vector close to the desired response. The proposed adaptation module is built using an incremental training named GA-AM algorithm (Growing-Adjustment Adaptation Module). Two adaptation strategies are applied : Growing and Adjustment. The growing criteria are based on the estimation of the significance of the new input and the significance of the nearest unit compared to the input. The adjustment consists of the update of two specific units (nearest and desired contributor) parameters using the standard LMS gradient descent to decrease the error at each time no new unit is allocated. This new training algorithm is evaluated by the adaptation of two handwriting recognition systems. The results, reported according to the cumulative error, show that the GA-AM algorithm leads to decreasing the classification error and to instantly adapting the recognition system to a specific user's handwriting. Performance comparison of GA-AM training algorithm with two other adaptation strategies, based on four writer-dependent datasets, are presented.