Survey and bibliography of Arabic optical text recognition
Signal Processing
Hidden Markov Models for Speech Recognition
Hidden Markov Models for Speech Recognition
Script Recognition Using Inhomogeneous P2DHMM and Hierarchical Search Space Reduction
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Baseline Estimation For Arabic Handwritten Words
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Arabic Handwriting Recognition Competition
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Segmentation and Pre-Recognition of Arabic Handwriting
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Arabic Handwriting Recognition Using Baseline Dependant Features and Hidden Markov Modeling
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
Arabic word recognition by classifiers and context
Journal of Computer Science and Technology
Histogram clustering and hybrid classifier for handwritten Arabic characters recognition
SPPRA'06 Proceedings of the 24th IASTED international conference on Signal processing, pattern recognition, and applications
Pattern Recognition Letters
Recognition of off-line printed Arabic text using Hidden Markov Models
Signal Processing
Region growing based segmentation algorithm for typewritten and handwritten text recognition
Applied Soft Computing
WAV'09 Proceedings of the 3rd WSEAS international symposium on Wavelets theory and applications in applied mathematics, signal processing & modern science
Classifiers combination and syntax analysis for Arabic literal amount recognition
Engineering Applications of Artificial Intelligence
HMM-based system for recognizing words in historical Arabic manuscript
International Journal of Robotics and Automation
Human reading based strategies for off-line Arabic word recognition
SACH'06 Proceedings of the 2006 conference on Arabic and Chinese handwriting recognition
A two-tier Arabic offline handwriting recognition based on conditional joining rules
SACH'06 Proceedings of the 2006 conference on Arabic and Chinese handwriting recognition
Databases and competitions: strategies to improve Arabic recognition systems
SACH'06 Proceedings of the 2006 conference on Arabic and Chinese handwriting recognition
Offline handwritten Arabic cursive text recognition using Hidden Markov Models and re-ranking
Pattern Recognition Letters
Mono-font cursive arabic text recognition using speech recognition system
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Using diversity in classifier set selection for arabic handwritten recognition
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
Offline handwritten arabic character segmentation with probabilistic model
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
Offline arabic handwritten text recognition: A Survey
ACM Computing Surveys (CSUR)
KHATT: An open Arabic offline handwritten text database
Pattern Recognition
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An offline recognition system for Arabic handwrittenwords is presented. The recognition system is based ona semi-continuous 1-dimensional HMM. From each binaryword image normalization parameters were estimated. Firstheight, length, and baseline skew are normalized, then featuresare collected using a sliding window approach. Thispaper presents these methods in more detail. Some parameterswere modified and the consequent effect on the recognitionresults are discussed. Significant tests were performedusing the new IFN/ENIT - database of handwritten Arabicwords. The comprehensive database consists of 26459Arabic words (Tunisian town/village names) handwrittenby 411 different writers and is free for non-commercial research.In the performed tests we achieved maximal recognitionrates of about 89% on a word level.