Abjad Hawwaz: An Offline Arabic Handwriting Recognition System

2005

—Among the vast range of off–line optical characterrecognition applications is the machine processing of forms. Theobjective is to imitate the human ability to read but at higher speed.This paper presents a neural network based system to recognisehandwritten Arabic characters collected from more than 500 forms.Wavelet coefficients extracted from the character samples are used totune the neural network weights. The performance of the system isassessed using a corpus that includes 15800 character samples. Keywords —Neural Networks, Pattern Recognition, Image Pro-cessing and Understanding, Arabic OCR. I. INTRODUCTION A RABIC language provides a rich source of technicalchallenges for recognition algorithms. The most obviouscharacteristics of the Arabic language is that Arabic scripts areinherently cursive, though in this paper we are not tackling thisissue. The Arabic alphabet consists of 28 basic letters, whichconsists of strokes and dots. Ten of them have one dot, threehave two dots, t...

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.

2

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.

Pattern Recognition, 2010

Off-line Arabic handwriting recognition and segmentation has been a popular field of research for many years. It still remains an open problem. The challenging nature of handwriting recognition and segmentation has attracted the attention of researchers from industry and academic circles. Recognition and segmentation of Arabic handwritten script is a difficult task because the Arabic handwritten characters are naturally both cursive and unconstrained. The analysis of Arabic script is more complicated in comparison with English script. It is believed, good segmentation is one reason for high accuracy character recognition. This paper proposes and investigates four main segmentation techniques. First, a new feature-based Arabic heuristic segmentation AHS technique is proposed for the purpose of partitioning Arabic handwritten words into primitives (over-segmentations) that may then be processed further to provide the best segmentation. Second, a new feature extraction technique (modified direction features-MDF) with modifications in accordant with the characteristics of Arabic scripts is also investigated for the purpose of segmented character classification. Third, a novel neuralbased technique for validating prospective segmentation points of Arabic handwriting is proposed and investigated based on direction features. In particular, the vital process of handwriting segmentation is examined in great detail. The classifier chosen for segmentation point validation is a feed-forward neural network trained with the back-propagation algorithm. Many experiments were performed, and their elapsed CPU times and accuracies were reported. Fourth, new fusion equations are proposed and investigation to examine and evaluate a prospective segmentation points by obtaining a fused value from three neural confidence values obtained from right and center character recognition outputs in addition to the segmentation point validation (SPV) output. Confidence values are assigned to each segmentation point located through feature detection. All techniques components are tested on a local benchmark database. High segmentation accuracy is reported in this research along with comparable results for character recognition and segmentation.

In this paper we present a novel segmentation-free Arabic handwriting recognition system based on hidden Markov model (HMM). Two main contributions are introduced: a novel pre-processing method and a new technique for dividing the image into non uniform horizontal segments to extract the features. The proposed system first pre-processes the input image by setting the thickness of the input word to three pixels and fixing the spacing between the different parts of the word. The input image is then divided into constant number of non uniform horizontal segments depending on the distribution of the foreground pixels. A set of robust features representing the foreground pixels is extracted using vertical sliding windows. The proposed system builds character HMM models and learns word HMM models using embedded training data. The performance of the proposed system is very promising compared with other Arabic handwriting recognition systems available in the literature.

ACS/IEEE International Conference on Computer Systems and Applications, 2003. Book of Abstracts., 2003

The problem of handwritten character recognition is still a big challenge for the scientific community. Several approaches have been attempted in past years with this purpose in mind, but most of these focused on the English pre-printed or handwritten characters space. For this purpose, this research has been done to shed some light over the Arabic handwritten text recognition.

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...

2002

The segmentation and recognition of Arabic handwritten text has been an area of great interest in the past few years. However, a small number of research papers and reports have been published in this area. There are several major problems with Arabic handwritten text processing: Arabic is written cursively and many external objects are used such as dots, 'Hamza', 'Madda', and diacritic objects. In addition, Arabic characters have more than one shape according to their position inside a word. More than one character can also share the same horizontal space, creating vertically overlapping connected or disconnected blocks of characters. This makes the problem of segmentation of Arabic text into characters, and their classification even more difficult. In this work a technique is presented that segments difficult handwritten Arabic text. A conventional algorithm is used for the initial segmentation of the text into connected blocks of characters. The algorithm then generates pre-segmentation points for these blocks. A neural network is subsequently used to verify the accuracy of these segmentation points. Another conventional algorithm uses the verified segmentation points and segments the connected blocks of characters. These characters can then be used as input to another neural network for classification.

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.