Toufik Sari

Off-line handwritten Arabic character segmentation algorithm: ACSA

Character segmentation is a necessary preprocessing step for character recognition in many OCR systems. It is an important step because incorrectly segmented characters are unlikely to be recognized correctly. The most difficult case in character segmentation is the cursive script. The scripted nature of Arabic written language poses some high challenges for automatic character segmentation and recognition. In this paper, a new character segmentation algorithm (ACSA) of Arabic scripts is presented. The developed segmentation algorithm yields on the segmentation of isolated handwritten words in perfectly separated characters. It is based on morphological rules, which are constructed at the feature extraction phase. Finally, ACSA is combined with an existing handwritten Arabic character recognition system (RECAM).

Rule Based Neural Networks Construction for Handwritten Arabic City-Names Recognition

A recent innovation in artificial intelligence research has been the integration of multiple techniques into hybrid systems. These systems seek to overcome the deficiencies of traditional artificial techniques by combining techniques with complementary capabilities. At the crossroads of symbolic and neural processing, researchers have been actively investigating the synergies that might be obtained from combining the strengths of these two paradigms. In this article, we deal with a knowledge based artificial neural network for handwritten Arabic city-names recognition. We start with words perceptual features analysis in order to construct a hierarchical knowledge base reflecting words description. A translation algorithm then converts the symbolic representation into a neural network, which is empirically trained to overcome the handwriting variability.

Cursive Arabic Script Segmentation and Recognition System

Abstract Character segmentation is a necessary preprocessing step for character recognition in many OCR systems. It is an important step because incorrectly segmented characters will not be recognized correctly. The most difficult case in character segmentation is cursive script. The scripted nature of Arabic written language poses some high challenges for automatic character segmentation and recognition. The authors present a new Character Segmentation Algorithm (ACSA) of Arabic script. The developed segmentation algorithm yields the splitting up of isolated handwritten words in perfectly separated characters. It is based on topological rules, which are constructed at the feature extraction phase. To increase ACSA’s performances, it was combined it with an Arabic characters recognition system, RECAM.

MOrpho-LEXical analysis for correcting OCR-generated Arabic words (MOLEX)

In this paper we present a contextual-based method for correcting Arabic words generated by OCR systems. This technique operates as a post-processor and it wants to be universal. It corrects substitution and rejection errors. The Arabic language properties are very useful in morpho-lexical analysis and therefore they are strongly exploited in the development of the method. The substitution errors, the most frequently committed ones by the OCR systems, are rewritten in production rules to be used by a rule-based system for correcting Arabic words. The first version of the developed method operates only at the morpho-lexical level, the extension to the other levels of language analysis is considered in perspectives.

Text extraction from historical document images by the combination of several thresholding techniques

This paper presents a new technique for the binarization of historical document images characterized by deteriorations and damages making their automatic processing difficult at several levels. The proposed method is based on hybrid thresholding combining the advantages of global and local methods and on the mixture of several binarization techniques. Two stages have been included. In the first stage, global thresholding is applied on the entire image and two different thresholds are determined from which the most of image pixels are classified into foreground or background. In the second stage, the remaining pixels are assigned to foreground or background classes based on local analysis. In this stage, several local thresholding methods are combined and the final binary value of each remaining pixel is chosen as the most probable one. The proposed technique has been tested on a large collection of standard and synthetic documents and compared with well-known methods using standard measures and was shown to be more powerful.

State-of-the-art of Off-line Arabic Handwriting Segmentation

Computer processing of off-line Arabic handwriting is very difficult. The cursiveness of the script and the almost variability of the handwritten symbols make the automatic recognition of Arabic a very challenging task. Character segmentation is an important pre-processing stage in any intelligent character recognition system. This paper surveys the field of off-line Arabic character segmentation. Four classes of segmentation approaches are identified based on the used features and on where segmentation points are located. Text curves analysis, outer contour detection and following, stroke scrutiny and singularities vs regularities are the most used techniques. Instructive examples of each category are described and some comments are given.

Tool for automatic tuning of binarisation techniques

Most of the proposed binarisation methods include parameters that must be set correctly before use. The determination of the values of these parameters is made most of the time manually after several tests. However, the optimum parameter values differ from an image to another and therefore the parameterisation shall be carried out for each image separately. In fact, as this task is very difficult, even impossible for large collections of images, the tuning is usually done once for the entire image collection. In this study, the authors propose a tool for automatic and adaptive parameterisation of binarisation techniques for each image separately. The adopted methodology is based on the use of an artificial neural network (ANN) to learn the optimal parameter values of a binarisation method for a set of images (training set), based on their features, and to use the trained ANN to determine the optimal parameter values for other images not learned. Several experiments have been conducted on images of degraded documents and the obtained results are encouraging.

Structural feature-based evaluation method of binarization techniques for word retrieval in the degraded Arabic document images

One of the most important and necessary steps in the process of document analysis and recognition is the binarization, which allows extracting the foreground from the background. Several binarization techniques have been proposed in the literature, but none of them was reliable for all image types. This makes the selection of one method to apply in a given application very difficult. Thus, performance evaluation of binarization algorithms becomes therefore vital. In this paper, we are interested in the evaluation of binarization techniques for the purpose of retrieving words from the images of degraded Arabic documents. A new evaluation methodology is proposed. The proposed evaluation methodology is based on the comparison of the visual features extracted from the binarized document images with ground truth features instead of comparing images between themselves. The most appropriate thresholding method for each image is the one for which the visual features of the identified words in the image are “closer” to the features of the reference words. The proposed technique was used here to assess the performances of eleven algorithms based on different approaches on a collection of real and synthetic images.

A Connectionist Approach for Adaptive Lesson

This paper investigates the use of computational intelligence for adaptive lesson generation in a distance learning environment. A method for adaptive pedagogical hypermedia document generation is proposed and implemented in a prototype called KnowledgeClass. This method is based on specialized connectionist architecture. The domain model is represented in a connectionist-based system which provides an optimal didactic plan composed of a set of basic units. The generated didactic plan is adapted to the learner’s goals, abilities and preferences.