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IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Neuro-Heuristic Approach for Segmenting Handwritten Arabic Text
AICCSA '01 Proceedings of the ACS/IEEE International Conference on Computer Systems and Applications
Techniques for Language Identification for Hybrid Arabic-English Document Images
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Recognising handwritten Arabic manuscripts using a single hidden Markov model
Pattern Recognition Letters
Recognition of Printed Urdu Script
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
HMM Based Approach for Handwritten Arabic Word Recognition Using the IFN/ENIT- Database
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Adaptive Dissection Based Subword Segmentation of Printed Arabic Text
IV '05 Proceedings of the Ninth International Conference on Information Visualisation
Pre-processing Methods for Handwritten Arabic Documents
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Offline Arabic Handwriting Recognition: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Programming pattern recognition
AFIPS '55 (Western) Proceedings of the March 1-3, 1955, western joint computer conference
Offline arabic handwritten text recognition: A Survey
ACM Computing Surveys (CSUR)
Segmentation of connected handwritten digits using Self-Organizing Maps
Expert Systems with Applications: An International Journal
Applied Numerical Mathematics
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This paper presents a new technique of high accuracy to recognize both typewritten and handwritten English and Arabic texts without thinning. After segmenting the text into lines (horizontal segmentation) and the lines into words, it separates the word into its letters. Separating a text line (row) into words and a word into letters is performed by using the region growing technique (implicit segmentation) on the basis of three essential lines in a text row. This saves time as there is no need to skeletonize or to physically isolate letters from the tested word whilst the input data involves only the basic information-the scanned text. The baseline is detected, the word contour is defined and the word is implicitly segmented into its letters according to a novel algorithm described in the paper. The extracted letter with its dots is used as one unit in the system of recognition. It is resized into a 9x9 matrix following bilinear interpolation after applying a lowpass filter to reduce aliasing. Then the elements are scaled to the interval [0,1]. The resulting array is considered as the input to the designed neural network. For typewritten texts, three types of Arabic letter fonts are used-Arial, Arabic Transparent and Simplified Arabic. The results showed an average recognition success rate of 93% for Arabic typewriting. This segmentation approach has also found its application in handwritten text where words are classified with a relatively high recognition rate for both Arabic and English languages. The experiments were performed in MATLAB and have shown promising results that can be a good base for further analysis and considerations of Arabic and other cursive language text recognition as well as English handwritten texts. For English handwritten classification, a success rate of about 80% in average was achieved while for Arabic handwritten text, the algorithm performance was successful in about 90%. The recent results have shown increasing success for both Arabic and English texts.