Arabic handwriting recognition using structural and syntactic pattern attributes (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.
Recognition of Offline Handwritten Arabic Words Using a Few Structural Features
Computers, Materials & Continua, 2021
Handwriting recognition is one of the most significant problems in pattern recognition, many studies have been proposed to improve this recognition of handwritten text for different languages. Yet, Fewer studies have been done for the Arabic language and the processing of its texts remains a particularly distinctive problem due to the variability of writing styles and the nature of Arabic scripts compared to other scripts. The present paper suggests a feature extraction technique for offline Arabic handwriting recognition. A handwriting recognition system for Arabic words using a few important structural features and based on a Radial Basis Function (RBF) neural networks is proposed. The methods of feature extraction are central to achieve high recognition performance. The proposed methodology relies on a feature extraction technique based on many structural characteristics extracted from the word skeleton (subwords, diacritics, loops, ascenders, and descenders). In order to reach our purpose, we built our own word database and the proposed system has been successfully tested on a handwriting database of Algerian city names (wilayas). Finally, a simple classifier based on the radial basis function neural network is presented to recognize certain words to verify the reliability of the proposed feature extraction. The experiments on some images of the benchmark IFN/ENIT database show that the proposed system improves recognition and the results obtained are indicative of the efficiency of our technique.
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...
The handwriting is one of the most familiar communication media. With the rapid advancements in Information Technology and the development of portable and wearable computers, Pen-based interfaces with automatic handwriting recognition offer a very easy and natural input method. Basic stages to build a system that tackles the problem of recognizing online Arabic cursive handwriting are proposed here. In general, this study addresses the different methods and techniques that can be used in different stages of the recognition system and how the different combinations affect the recognition accuracy.
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.
Review of feature extraction techniques for offline handwriting Arabic text recognition
2014
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 techniques.
Arabic Handwriting Recognition Based on Classifier Combination
2015
Handwriting recognition is a rich and complex issue. Some of its problems include the large shape variations in human handwriting. Classifier combination contributes in increasing the classification accuracy compared to the performance of individual classifier. In this paper, we present an online handwriting recognizer based on classifier combination according to holistic approach. We propose two combination types: a combination between online recognition and offline recognition, and a combination between dynamic approach, structural approach and statistical approach. For feature extraction phase and classification phase, we use Point Features (PF) and Dynamic Time Warping (DTW) in dynamic approach, Freeman Chain code (FC) and Levenshtein Distance (LD) in structural approach, Zernike Moments (ZM) and Support Vector Machine (SVM) in statistical approach. In the combination phase, different methods are applied on the results provided by the three classifiers and different combinations...
Offline Arabic Handwriting Recognition: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006
The automatic recognition of text on scanned images has enabled many applications such as searching for words in large volumes of documents, automatic sorting of postal mail, and convenient editing of previously printed documents. The domain of handwriting in the Arabic script presents unique technical challenges and has been addressed more recently than other domains. Many different methods have been proposed and applied to various types of images. This paper provides a comprehensive review of these methods. It is the first survey to focus on Arabic handwriting recognition and the first Arabic character recognition survey to provide recognition rates and descriptions of test data for the approaches discussed. It describes background on the field, discussion of the methods, and future research directions.
An Approach Based on Structural Segmentation for the Recognition of Arabic Handwriting
INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences, 2010
In this paper we propose a new segmentation approach applied to Arabic handwriting, which can reconstruct in offline a tracing path similar to that in the case of online. Our approach uses a semiskeletonization technique for following lines and calculation of the characteristics of characters. With the application of an SVM classifier in the classification phase, we were able to achieve very interesting recognition rates in reduced time. When compared to similar works, this work is very interesting for the Off-line recognition of Arabic handwriting.
Recognition-Based Segmentation Algorithm for On-Line Arabic Handwriting
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.