A Method of Recognition of Arabic Cursive Handwriting

Automatic off-line Arabic handwriting recognition still faces a big challenges. Due to the cursive nature of the Arabic language, most of published works are based on recognition of a whole word without segmentation. This paper presents a new framework for the recognition of handwritten Arabic words based on segmentation. This framework involves two phases (training phase and testing phase). In the training phase, Arabic handwritten characters were trained to be recognized, while in the testing phase, words were segmented into characters for recognition. Classification is achieved in two steps (classification of the segmented characters and classification of the word). A dictionary is constructed and used to correct any errors occurring during the previous stages of the recognition process. This work has been tested with IFN/ENIT database and a comparison made against some existing methods and promising results have been obtained.

IIUM Press eBooks, 2011

Optical Character Recognition (OCR) is the process of recognizing printed or handwritten text on paper documents. This paper proposes an OCR system for Arabic characters. In addition to the preprocessing phase, the proposed recognition system consists mainly of three phases. In the first phase, we employ word segmentation to extract characters. In the second phase, Histograms of Oriented Gradient (HOG) are used for feature extraction. The final phase employs Support Vector Machine (SVM) for classifying characters. We have applied the proposed method for the recognition of Jordanian city, town, and village names as a case study, in addition to many other words that offers the characters shapes that are not covered with Jordan cites. The set has carefully been selected to include every Arabic character in its all four forms. To this end, we have built our own dataset consisting of more than 43.000 handwritten Arabic words (30000 used in the training stage and 13000 used in the testing stage). Experimental results showed a great success of our recognition method compared to the state of the art techniques, where we could achieve very high recognition rates exceeding 99%.

SN computer science, 2020

There are much heavy studies on handwritten character recognition (HCR) for nearly previous four decades. The research on some of the common script like Arabic, Indian and Chinese has been done. This manuscript presents a survey of character recognition on Arabic script, and most of the popular published paper methods are summarized and also analyzed different methods for building a robust system of HCR and included some future research on recognition direction of handwritten character. The paper analyzed and presented various algorithms with respect to preprocessing methods, segmentation methods, feature extraction methods and various classification approaches of the Arabic character recognition.

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.

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.

2007

Despite of the decent development of the pattern recognition science applications in the last decade of the twentieth century and this century, text recognition remains one of the most important problems in pattern recognition. To the best of our knowledge, little work has been done in the area of Arabic text recognition compared with those for Latin, Chins and Japanese text. The main difficulty encountered when dealing with Arabic text is the cursive nature of Arabic writing in both printed and handwritten forms. An Automatic Arabic Hand-Written Text Recognition (AHTR) System is proposed. An efficient segmentation stage is required in order to divide a cursive word or sub-word into its constituting characters. After a word has been extracted from the scanned image, it is thinned and its base line is calculated by analysis of horizontal density histogram. The pattern is then followed through the base line and the segmentation points are detected. Thus after the segmentation stage, the cursive word is represented by a sequence of isolated characters. The recognition problem thus reduces to that of classifying each character. A set of features extracted from each individual characters. A minimum distance classifier is used. Some approaches are used for processing the characters and post processing added to enhance the results. Recognized characters will be appended directly to a word file which is editable form.

SN Computer Science, 2020

The issue of handwritten recognition in Arabic script nature has attracted many researchers from both academic and industrial fields. But their efforts have not reached satisfying outcomes till now. In this paper, a survey has been done in segmentation and recognition of handwritten documents in Arabic script. Most of the previous published works have been analyzed, and some remedies have been suggested. Various strategies used for creating a powerful recognition system have been summarized. This paper presents various algorithms with respect to text, word and characters segmentation and recognition of Arabic document. It analyzes the recognition stage of Arabic script depending on segmentation strategies.

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.

The issue of handwritten character recognition is still a big challenge to the scientific community. Several approaches to address this challenge have been attempted in the last years, mostly focusing on the English pre-printed or handwritten characters space. Thus, the need to attempt a research related to Arabic handwritten text recognition. Algorithms based on neural networks have proved to give better results than conventional methods when applied to problems where the decision rules of the classification problem are not clearly defined. Two neural networks were built to classify already segmented characters of handwritten Arabic text. The two neural networks correctly recognized 73% of the characters. However, one hurdle was encountered in the above scenario, which can be summarized as follows: there are a lot of handwritten characters that can be segmented and classified into two or more different classes depending on whether they are looked at separately, or in a word, or even in a sentence. In other words, character classification, especially handwritten Arabic characters, depends largely on contextual information, not only on topographic features extracted from these characters.

2009 10th International Conference on Document Analysis and Recognition, 2009

In this paper, we introduce an on-line Arabic handwritten recognition system based on new stroke segmentation algorithm. The proposed algorithm uses an over segmentation method that has the advantage of giving all correct segments at least. It is based on arbitrary segmentation followed by segmentation enhancement, consecutive joints connection and finally segmentation point locating. The proposed system gives an excellent recognition rate up to 97% and 92% for words and letter recognition.