Survey and bibliography of Arabic optical text recognition
Signal Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Omnifont Open-Vocabulary OCR System for English and Arabic
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Offline General Handwritten Word Recognition Using an Approximate BEAM Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Omnifont and Unlimited-Vocabulary OCR for English and Arabic
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Recognition of English and Arabic Numerals Using a Dynamic Number of Hidden Neurons
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 3)-Volume 3 - Volume 3
Recognition of Off-Line Handwritten Arabic Words Using Hidden Markov Model Approach
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
A Neuro-Heuristic Approach for Segmenting Handwritten Arabic Text
AICCSA '01 Proceedings of the ACS/IEEE International Conference on Computer Systems and Applications
Arabic Hand-Written Text Recognition
AICCSA '01 Proceedings of the ACS/IEEE International Conference on Computer Systems and Applications
Hand-Written Indian Numerals Recognition System Using Template Matching Approaches
AICCSA '01 Proceedings of the ACS/IEEE International Conference on Computer Systems and Applications
Script-Independent, HMM-Based Text Line Finding for OCR
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Printed arabic character recognition using HMM
Journal of Computer Science and Technology
Recognition of Persian handwritten digits using image profiles of multiple orientations
Pattern Recognition Letters
Offline Arabic Handwriting Recognition: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition of Off-Line Handwritten Arabic Words Using Neural Network
GMAI '06 Proceedings of the conference on Geometric Modeling and Imaging: New Trends
A Novel Character Recognition Algorithm Based on Hidden Markov Models
AICI '09 Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence
Segment confidence-based binary segmentation (SCBS) for cursive handwritten words
Expert Systems with Applications: An International Journal
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
Handwritten character recognition system using a simple feature
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
Arabic handwriting recognition using structural and syntactic pattern attributes
Pattern Recognition
Offline arabic handwritten text recognition: A Survey
ACM Computing Surveys (CSUR)
Efficient ant colony optimization for image feature selection
Signal Processing
KHATT: An open Arabic offline handwritten text database
Pattern Recognition
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This paper describes a technique for the recognition of optical off-line handwritten Arabic (Indian) numerals using hidden Markov models (HMM). Features that measure the image characteristics at local, intermediate, and large scales were applied. Gradient, structural, and concavity features at the sub-regions level are extracted and used as the features for the Arabic (Indian) numeral. Several experiments were conducted for estimating the suitable number of image divisions, and the best combination of features using the HMM classifier. A number of experiments were conducted to estimate the best number of states and codebook sizes in terms of the highest recognition rate possible. In this work, we did not follow the general trend of using the sliding window technique with HMM. Instead, a multi-resolution feature extraction approach was implemented on the whole digit. A database of 44 writers, with 48 samples per digit resulting in a database of 21120 samples was used. The achieved average recognition rate is 99%. The classification errors were analysed and attributed to bad data, different writing styles of some digits, errors between digit pairs, and genuine errors. The presented technique, which is writer independent, proved to be effective in the automatic recognition of Arabic (Indian) numerals.