A Method of Recognition of Arabic Cursive Handwriting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Survey and bibliography of Arabic optical text recognition
Signal Processing
Recognition of off-line cursive handwriting
Computer Vision and Image Understanding
The Role of Holistic Paradigms in Handwritten Word Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition of Handwritten Cursive Arabic Characters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Off Line Arabic Character Recognition - A Survey
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
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
Combination of Local and Global Vision Modelling for Arabic Handwritten Words Recognition
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Off-Line Handwritten Arabic Character Segmentation Algorithm: ACSA
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Recognising handwritten Arabic manuscripts using a single hidden Markov model
Pattern Recognition Letters
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
Pattern Recognition Letters
Offline Recognition of Unconstrained Handwritten Texts Using HMMs and Statistical Language Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Offline Arabic Handwriting Recognition: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition of off-line printed Arabic text using Hidden Markov Models
Signal Processing
Offline arabic handwritten text recognition: A Survey
ACM Computing Surveys (CSUR)
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We propose a novel algorithm for the segmentation and pre-recognition of off-line handwritten Arabic text. Our character segmentation method over-segments each word, then removes extra breakpoints using knowledge of letter shapes. On a test set of 200 images, 92.3% of the segmentation points were detected correctly, with 5.1% instances of over-segmentation. The pre-recognition component annotates each detected letter with shape information, to be used for recognition in future work.