A new System for offline Printed Arabic Recognition for Large Vocabulary : SPARLV

This paper presents a contribution for the Arabic printed recognition. In fact, we are interested in the printed decomposable Arabic word recognition. The proposed system uses the analytical approach through the segmentation into characters to succeed to a generation of letter hypotheses as well as word hypotheses using a lexical verification in a pre-established dictionary of the language. Our proposed system SPARLV is able to put valid hypotheses of words thanks to the lexical verification

Kurdish Optical Character Recognition

Currently, no offline tool is available for Optical Character Recognition (OCR) in Kurdish. Kurdish is spoken in different dialects and uses several scripts for writing. The Persian/Arabic script is widely used among these dialects. The Persian/Arabic script is written from Right to Left (RTL), it is cursive, and it uses unique diacritics. These features, particularly the last two, affect the segmentation stage in developing a Kurdish OCR. In this article, we introduce an enhanced character segmentation based method which addresses the mentioned characteristics. We applied the method to text-only images and tested the Kurdish OCR using documents of different fonts, font sizes, and image resolutions. The results of the experiments showed that the accuracy rate of character recognition of the proposed method was 90.82% on average

A prototype system for handwritten sub-word recognition: Toward Arabic-manuscript transliteration

A prototype system for the transliteration of diacritics-less Arabic manuscripts at the sub-word or part of Arabic word (PAW) level is developed. The system is able to read sub-words of the input manuscript using a set of skeleton-based features. A variation of the system is also developed which reads archigraphemic Arabic manuscripts, which are dot-less, into archigraphemes transliteration. In order to reduce the complexity of the original highly multiclass problem of sub-word recognition, it is redefined into a set of binary descriptor classifiers. The outputs of trained binary classifiers are combined to generate the sequence of sub-word letters. SVMs are used to learn the binary classifiers. Two specific Arabic databases have been developed to train and test the system. One of them is a database of the Naskh style. The initial results are promising. The systems could be trained on other scripts found in Arabic manuscripts.Comment: 8 pages, 7 figures, 6 table

Off-Line Handwritten Arabic Characters Segmentation Using Slant-Tolerant Segment Features (STSF) [PJ6123. S562 2007 f rb].

Tema utama bagi kajian ini ialah pensegmenan aksara tulisan Arab luar talian. Suatu sistem pengecaman aksara tulisan Arab yang baik mampu meningkatkan kesalingtindakan antara manusia dengan komputer. The main theme of this research is the off-line handwritten Arabic characters segmentation. A successful handwritten Arabic character recognition system improves interactivity between the human and the computers