This paper describes an effort toward building Arabic cheque databases for research in

Abstract. The research on offline handwritten Arabic character recognition has received more and more attention in recent years, because of the increasing needs of Arabic document digitization. The variation in Arabic handwriting brings great difficulty in character segmentation and recognition, eg., the subparts (diacritics) of the Arabic character may shift away from the main part. In this paper, a new probabilistic segmentation model is proposed. First, a contour-based over-segmentation method is conducted, cutting the word image into graphemes. The graphemes are sorted into 3 queues, which are character main parts, sub-parts (diacritics) above or below main parts respectively. The confidence for each character is calculated by the probabilistic model, taking recognizer output, geometric confidence and logical constraint into consideration. Then, the global optimization is conducted to find optimal cutting path, taking weighted average of character confidences as objective function. Experiments on handwritten Arabic documents with various writing styles show the proposed method is effective. 1

Recognition of cursive handwritings such as Persian script is a hard task as there is no fixed segmentation and simultaneous segmentation and recognition is required. This paper presents a novel comparison method for such tasks which is based on a Multiple States Machine to perform robust elastic comparison of small segments with high speed through generation and maintenance of a set of concurrent possible hypotheses. The approach is implemented on Persian (Farsi) language using a typical feature set and a specific tailored genetic algorithm and the recognition and computation time is compared with dynamic programming comparison approach.

Arabic cursive optical character recognition (OCR) based on the dynamic time warping (DTW) algorithm provides simultaneously very interesting segmentation and recognition rates. However, the computing complexity of the DTW algorithm restricts its widespread utilization and its consideration at a commercial scale. Accelerating the DTW execution time has attracted many researchers and several solutions have already been proposed. These solutions are commonly based on very specialized processors and hardware architectures and as such they remain very expensive and not amenable to a large scale utilization. In a previous work, we found that loosely coupled architectures can indeed provide viable infrastructures to implement a distributed Arabic OCR. Our objective here is to allow the recognition of huge quantities of Arabic documents such as those of certain national libraries. Undoubtedly, enough processing power and storage capabilities are needed. In this paper, we proposed and used a peer-to-peer (P2P) architecture using the scientific research Tunisian grid (SRTG). Conducted experiments testify that our proposed architecture provides very adequate speedups of the DTWbased

Abstract: This paper presents a novel new technique based on feature extraction and on dynamic cursor sizing for the recognition of Arabic Text. The most challenging area in Arabic OCR (AOCR) research is the segmentation of words into their sub-words and their individual characters. Several rules are defined that govern the size and movement of the cursor through each segment. The features obtained from each segment are termed strokes and each segment is defined by a number of strokes where each stroke is defined mainly in terms of a sequence of directions. The basic concept followed here is a logical, dynamically sized cursor that is used to "travel " through a text image of one word at a time while extracting features of strokes. The strokes obtained are then "pieced " back together to be classified into character classes based on a knowledge base and eventual recognition of characters is achieved. The results demonstrate that the technique is successful.

Abstract—Persian (Farsi) script is totally cursive and each character is written in several different forms depending on its former and later characters in the word. These complexities make automatic handwriting recognition of Persian a very hard problem and there are few contributions trying to work it out. This paper presents a novel practical approach to online recognition of Persian handwriting which is based on representation of inputs and patterns with very simple visual features and comparison of these simple terms. This recognition approach is tested over a set of Persian words and the results have been quite acceptable when the possible words where unknown and they were almost all correct in cases that the words where chosen from a prespecified list.

Abstract: In spite of the diversity of printed Arabic optical character recognition products and proposals, the problem seems to be not yet well solved. The complex morphology and calligraphy of the Arabic writing on one hand and the use of some light approaches on the other hand are behind the poorness of these products. However, some strong proposed approaches didn’t find the opportunity to be commercialised because of generally their corresponding complex computing. The dynamic time warping algorithm is considered as one among these strong approaches. In fact, several studies and experiments have shown and confirmed that the printed Arabic optical character recognition based on dynamic time warping algorithm provides a very interesting recognition rate especially for large and huge vocabularies. One of the attractive sides of the dynamic time warping algorithm is its ability to recognize properly connected or cursive characters (words or sub words) without prior segmentation. Furthermore, this algorithm performs the recognition process from within a reference library of isolated characters and owns a very good immunity against noises. Unfortunately, the big amount of its computing during the recognition process makes its execution time very slow and, hence, restricts its utilization. Many researchers attempted to speedup the execution time of this algorithm. Unfortunately, the corresponding proposed solutions require generally specific high cost architectures. Loosely coupled architectures such as grapes or grid computing can provide enough power without additional cost to distribute the complexity of some greedy applications. Consequently, we report in this paper the performance analysis of an analytical and an experimental study of a distributed Arabic optical character recognition based

Abstract—Persian (Farsi) script is totally cursive and each character is written in several different forms depending on its former and later characters in the word. These complexities make automatic handwriting recognition of Persian a very hard problem and there are few contributions trying to work it out. This paper presents a novel practical approach to online recognition of Persian handwriting which is based on representation of inputs and patterns with very simple visual features and comparison of these simple terms. This recognition approach is tested over a set of Persian words and the results have been quite acceptable when the possible words where unknown and they were almost all correct in cases that the words where chosen from a prespecified list. P Keywords—Image Processing, Pattern Recognition.

Abstract: In this paper, an unexplored subject in the domains of Farsi/Arabic handwritten word preprocessing is introduced. Subwords play a vital role in many applications such as cheque amount recognition, text recognition, lexicon reduction and subword-based word recognition. Correcting the faults occurred in subwords will improve the overall performance of these applications. A subword is a connected-component in the main body of a word. The occurrence of a discontinuity in a subword, divides the subword into two isolated parts. These parts are detected as two incorrect subwords. In our algorithm, before correcting these faults, the baseline of each subword is corrected using the proposed baseline correction method. Then, to limit the exploration area in matching stage, the dots are removed. Undesirable discontinuities in subwords are detected by using a template matching algorithm. Disconnected parts of a subword are joined together by using three different methods. Experiments show that the cubic polynomial-based compensation method causes the best results and 2.87 % improvement in the subword recognition rate. Keywords: Detection, compensation, Farsi/Arabic subword, and cubic polynomial curve fitting.

In this paper, an efficient approach for the recognition of online Arabic handwritten characters is presented. The method employed involves three phases: First, pre-processing in which the original image is transformed into a binary image.Second, training neural networks with feed-forward back propagation algorithm.Finally, the recognition of the character through the use of Neural Network techniques. The proposed approach is tested on 1400 different characters written by ten users. Each user wrote 28 Arabic characters five times in order to get different writing variations. Experiment results showed the effectiveness of our approach for recognizing handwritten Arabic characters.