Ahmed M. Zeki

The Segmentation Problem in Arabic Character Recognition The State Of The Art

Arabic characters are used in several languages other than Arabic, despite to this fact; Arabic Character Recognition (ACR) has not received enough interests by researchers. Little researchprogress has been achieved comparing to the one done on the Latin or Chinese and the solutions available in the market are still far from being perfect. However, recent years have shown a considerable increase in the number of research papers. The cursive nature of Arabic writing makes the process of recognition a very challenging one. Several methods to segment the Arabic words into characters have been proposed in the past two decades. This paper seeks to provide a comprehensive review of the methods proposed by researchers to segment. There is a room for research in this area; hence, the speech aims at promoting the research among Muslim researchers to work on ACR by addressing the challenges posed by the nature of the characters. The segmentation methods are categorized in nine different methods based on the techniques used. The advantages and drawback of each one are discussed.

Segmentation techniques for online Arabic handwriting Recognition: A survey

This paper presents and compares techniques that have been used to segment Arabic handwriting scripts in online recognition systems. Those techniques attempt to segment cursive Arabic words into characters, or segment characters into small strokes that can be recognized via the recognition system. The structure and strategy of those reviewed techniques are explained in this article. The strengths and weaknesses of using these techniques will also be discussed.

Feature extraction techniques of online handwriting arabic text recognition

Online recognition of Arabic handwritten text has been an ongoing research problem for many years. Generally, online text recognition field has been gaining more interest lately due to the increasing popularity of hand-held computers, digital notebooks and advanced cellular phones. Most of the online text recognition systems consist of three main phases which are preprocessing, feature extraction, and recognition phase. This paper compares between different techniques that have been used to extract the features of Arabic handwriting scripts in online recognition systems. Those techniques attempt to extract the feature vector of Arabic handwritten words, characters, numbers or strokes. This vector then will be fed into the recognition engine to recognize the pattern using the feature vector. The structure and strategy of those reviewed techniques are explained in this article. The strengths and weaknesses of using these techniques will also be discussed.

Recognition Techniques for Online Arabic Handwriting Recognition Systems

Online recognition of Arabic handwritten text has been an on-going research problem for many years. Generally, online text recognition field has been gaining more interest lately due to the increasing popularity of hand-held computers, digital notebooks and advanced cellular phones. However, different techniques have been used to build several online handwritten recognition systems for Arabic text, such as Neural Networks, Hidden Markov Model, Template Matching and others. Most of the researches on online text recognition have divided the recognition system into these three main phases which are preprocessing phase, feature extraction phase and recognition phase which considers as the most important phase and the heart of the whole system. This paper presents and compares techniques that have been used to recognize the Arabic handwriting scripts in online recognition systems. Those techniques attempt to recognize Arabic handwritten words, characters, digits or strokes. The structure and strategy of those reviewed techniques are explained in this article. The strengths and weaknesses of using these techniques will also be discussed.

Problems of writing on digital surfaces in online handwriting recognition systems

Nowadays, keyboards and mice are the basic input devices for personal computers. Nevertheless, these devices may not remain as the only way of transmitting electronic data to computers. Other methods may become necessary, particularly with regard to the size of newer mobile devices and the method of transmission. Hand-held computers, mobile technology, for example, present significant opportunities for alternative devices that work in forms smaller than the traditional keyboard and mouse. In addition, the need for more natural human-machine interfaces becomes more important due to computer usage reaches a larger number of people worldwide. However, these new ways of human-machine interfaces have some problems while connecting to the computers. This paper presents and highlights some of these problems in the field of script recognition. This paper also illustrates the used techniques to overcome these matters.

New primitives to reduce the effect of noise for handwritten features extraction

A method for feature extraction for a handwritten OCR system is presented. In order to reduce the effect of the noise which is either an original noise or obtained as a result of the preprocessing stages, there is a need to develop a feature extraction method invariant to the expected distortions, and less dependent on the locations of high probable appearance of noise and distortion. This method depends only on the two primitive features: straight lines and curves. A chain code has been built from the thinned shape of the character. Two rules have been introduced to cut this chain code into small segments. From each segment one feature is defined and for each input character, a feature vector will be built. The prototype system was tested for alphanumeric characters and the results were satisfactory.

Skeleton extraction: Comparison of five methods on the Arabic IFN/ENIT database

Thinning “Skeletonization” is a very crucial stage in the Arabic Character Recognition (ACR) system. It simplifies the text shape and reduces the amount of data that needs to be handled and it is usually used as a pre-processing stage for recognition and storage systems. The skeleton of Arabic text can be used for: baseline detection, character segmentation, and features extraction, and ultimately supporting the classification. In this paper, five of the state of the art thinning algorithms are selected and implemented. The five algorithms are: SPTA, Zhang-Suen parallel thinning algorithm, Huang-Wan-Liu thinning algorithm, thinning and skeletonization based morphological operation algorithms. The five selected algorithms are applied on the IFN/ENIT dataset. The results obtained by the five methods are discussed and analyzed against the IFN/ENIT dataset based on preserving shape and the text connectivity, preventing spurious tails, maintaining one pixel width skeleton and avoiding the necking problem as well as running time efficiently. In addition to that some performance measurement for checking text connectivity, spurious tails and calculating the stroke thickness are proposed and carried out.

Online Recognition System for Handwritten Hindi Digits Based on Matching Alignment Algorithm

Recently, online character recognition approaches are gotten attention everywhere due to the raped growing of touch screen devices industry. Furthermore, keyboards and mice devises become inapplicable to be included in small devises. These reasons would open the gate for discovering new techniques which can enrich and enhance this kind of approaches. These online approaches can be used for recognizing different texts like letters, digits, or symbols. In this paper, an online system for recognizing handwritten Hindi digits is highlighted based on matching alignment algorithm. It illustrates every phase of the system in details which are: digits acquisition, preprocessing, features extraction, and recognition phase. The dataset of the system were collected by 50 writers using a touch screen laptop with 50 sample of each digit. The results of testing the proposed system showed a high accuracy rate with an average of 96%.

Computer security and ethics awareness among IIUM students: An empirical study

Peoples perception and attitude towards computer ethics and information security significantly affect the way they use information technology. This is especially the case among university students who are generally regarded as major violators of computer ethics and computer security. This paper follows previous work on computer security and ethics. The paper intend to examine the level of ethical and security awareness among IT and education students. The findings of this study reveals that there are satisfactory levels of awareness among the students surveyed with slightly higher level of awareness among IT students, most likely due to security and ethics courses they offered. Also, the findings indicate that gender-wise male students reported higher level of computer security and ethics violations than their female counterparts. The paper concludes that female students are more conscious of security and ethics while using computer (e.g. Internet) than the male students. Furthermore, IT students were found to be more aware of internet security & ethics, but largely ignore this knowledge and are more engaged in unethical activities and illegal internet practices when compared with Education students.

Online Recognition System for Handwritten Arabic Chemical Symbols

Arabic chemical symbols are remarkably different from Latin chemical symbols which written by Arabic characters. On the other hand, Arabic chemical symbols follow Latin chemical symbols from the structure of writing the symbols. Although, Arabic symbols have special way of the writing like writing direction, cursive style, and written by Arabic characters. In fact, these symbols are being used in schools and high education level around many Arabic countries. In this paper an online system for recognizing handwritten Arabic chemical symbols is presented. The paper illustrates each phase of the system in details. The system is dealing with some Arabic chemical symbols as initial study and to be updated with more symbols later.