Offline Arabic Handwriting Recognition: A Survey (original) (raw)
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Abjad Hawwaz: An Offline Arabic Handwriting Recognition System
International Journal of Computers and Applications, 2005
In this work we present a system for the recognition of handwritten Arabic text using neural networks. This work builds upon previous work done by that dealt with the vertical segmentation of the written text. However, faced with some problems like overlapping characters that share the same vertical space, we tried to fix that problem by performing horizontal segmentation. In this research we will use two basic neural networks to perform the task; the first one identifies blocks that need to be horizontally segmented, and the second one performs the horizontal segmentation. Both networks use a set of features that are extracted using a heuristic program. The system was tested and the rate of recognition obtained was over 90%. This strongly supports the usefulness of proposed measures for handwritten Arabic text.
A New Approach for Arabic Offline Handwriting Recognition
2008
A new approach for Arabic handwriting recognition is proposed. The proposed method is part of a larger software framework to teach Arabic reading and writing to illiterates. The method is customized to each letter of the Arabic alphabet. The characteristics of each letter are analyzed and the appropriate detection scheme for that letter is then determined. This allows the method to provide feedback to the user on the correctness of the character written. Furthermore, in case of incorrect writing, the method indicates what part of the letter was erroneously written. This feedback feature allows the user to enhance his handwriting the next time he writes the same letter. The target is to combat adult illiteracy in the Arab world by using Information Technology.
Online handwriting recognition for the Arabic letter set
Proceedings of the 5th WSEAS …, 2011
Automated methods for the recognition of Arabic script are at an early stage compared to their equivalent for the recognition of Latin and Chinese languages, especially of online handwriting recognition. In this paper we describe the stages of the recognition process unique to the Arabic hand written text. We also introduce an account of Arabic online handwriting recognition methods in literature, with a rich list of references for the interested readers. We cast some light on the characteristics of Arabic writing and present an overview of the common stages normally followed by handwriting recognition systems which are: preprocessing, segmentation, feature extraction, classification, and post-processing along with the most used techniques.
1993
Offline recognition of Arabic handwritten texts has been an ongoing research problem for many years. Generally, offline text recognition field has been gaining more interest lately due to an essential role in many human computer interaction applications including cheque verification, mail sorting or office automation. Most of the offline text recognition systems can be broken down into the following stages: pre-processing, feature extraction and also classification. This paper compares between different techniques that have been used to extract the features of Arabic handwriting scripts in offline recognition systems. Those methods attempt to extract the feature vector of Arabic handwritten characters, words, numbers or strokes. This vector then will be used in the recognition engine to recognize the pattern using the feature vector. The strategy and structure of those reviewed techniques are explained in this article. We will also discuss the weaknesses and strengths of using these...
Arabic Online Handwriting Recognition: A Survey
ACM, 2017
Nowadays, Arabic handwriting recognition is an active research area. The optical character recognition is classified into two approaches offline and online. There are many studies and applications for Arabic offline recognition, both typed and handwritten, yet there are few studies on Arabic Online recognition. Online recognition, in general, is oriented to only handwritten. The cursive, shapes, dots and delayed strokes of Arabic letters are the most challenging tasks to develop and improve an online system for the Arabic language. Moreover, handwriting of many Arab people becomes poor with low handwriting skills, especially after the cancellation of the Arabic calligraphy subject in the educational system in many Arabic countries. This paper presents a comprehensive survey on Arabic online handwriting recognition for the past few years. The paper aims to elevate the research in this subject, reveal the avenues for improving the recognition of Arabic online handwriting and enhance the skills of the Arab people in handwriting via online teaching and training system.
Arabic Handwritten Text Recognition Systems Challenges and Opportunities
Egyptian Journal of Language Engineering, 2023
Arabic handwritten text recognition faces significant challenges despite the large number of Arabic speakers. A critical review paper has analyzed previous research in this field, identifying problem areas and challenges faced by researchers. The paper focuses on trends in offline handwriting recognition systems and the unique characteristics of the Arabic language that pose technical challenges. The analysis involved comparing and contrasting previous research methods and performances to summarize critical problems and enumerate issues that must be addressed. The paper highlights several Arabic datasets that can be utilized as benchmarks for training, testing, and comparisons. These datasets are essential for evaluating the performance of Arabic handwriting recognition systems. Additionally, the paper concludes with a fundamental comparison and discussion of remaining open problems and trends in the field. It identifies several unresolved technical issues, such as the need for improved feature extraction and modeling techniques, as well as the need for large-scale, diverse datasets to facilitate better training and testing of Arabic handwriting recognition systems. Overall, the paper provides a comprehensive overview of the challenges and issues facing Arabic handwriting recognition and highlights areas where further research is needed.
Automatic recognition of handwritten Arabic characters: a comprehensive review
The paper is a comprehensive review of the current research trends in the area of Arabic language especially state-of-the-art approaches to highlight the current status of diverse research aspects of that area to facilitate the adaption and extension of previous systems into new applications and systems. The Arabic language has deep, widespread and unexplored scope to research although the tremendous effort and researches that had been done previously. Modern state-of-the-art methods and approaches with fewer errors are required according to the high speed of hardware and technology development. The focus of this article will be on the offline Arabic handwritten text recognition as it is one of the most important topics in the Arabic scope. The main objective of this paper is critically analyzing the current researches to identify the problem areas and challenges faced by the previous researchers. This identification is intended to provide many recommendations for future advances in the area. It also compares and contrasts technical challenges, methods and the performances of handwritten text recognition previous researches works. It summarizes the critical problems and enumerates issues that should be considered when addressing these tasks. It also shows some of the Arabic datasets that can be used as inputs and benchmarks for training, testing and comparisons. Finally, it provides a fundamental comparison and discussion of some of the remaining open problems and trends in that field.
ICDAR 2009 Arabic Handwriting Recognition Competition
2009
This paper describes the Arabic handwriting recognition competition held at ICDAR 2009. This third competition (the first was at ICDAR 2005 and the second at ICDAR 2007) again used the IfN/ENIT-database with Arabic handwritten Tunisian town names. Today, more than 82 research groups from universities, research centers, and industry are working with this database worldwide. This year, 7 groups with 17 systems were participating in the competition. The systems were tested on known data and on two data sets which are unknown to the participants. The systems were compared based on the most important characteristic: the recognition rate. Additionally, the relative speed of the different systems was compared. A short description of the participating groups, their systems, and the results achieved are finally presented.
ICDAR 2009 Online Arabic Handwriting Recognition Competition
2009 10th International Conference on Document Analysis and Recognition, 2009
This paper describes the Online Arabic handwriting recognition competition held at ICDAR 2009. This first competition uses the ADAB-database with Arabic online handwritten words. This year, 3 groups with 7 systems are participating in the competition. The systems were tested on known data (sets 1 to 3) and on one test dataset which is unknown to all participants (set 4). The systems are compared on the most important characteristic of classification systems, the recognition rate. Additionally, the relative speed of the different systems were compared. A short description of the participating groups, their systems, the experimental setup, and the performed results are presented.