Computer recognition of Arabic cursive scripts
Pattern Recognition
Fundamentals of digital image processing
Fundamentals of digital image processing
Fundamentals of speech recognition
Fundamentals of speech recognition
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
An HMM-Based Approach for Off-Line Unconstrained Handwritten Word Modeling and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
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
Off-Line Handwritten Arabic Character Segmentation Algorithm: ACSA
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Multi-Font Arabic Word Recognition Using Spectral Features
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
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
IEEE Transactions on Image Processing
Developing typewritten Arabic corpus with multi-fonts (TRACOM)
Proceedings of the International Workshop on Multilingual OCR
Offline arabic handwritten text recognition: A Survey
ACM Computing Surveys (CSUR)
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This paper presents an omni-font Arabic word recognition system. The system is based on multiple Hidden Markov Models (HMMs). Each word in the lexicon is represented with a distinct HMM. The proposed system first extracts a set of spectral features from word images, then uses those features to tune HMM parameters. The performance of the proposed system is assessed using a corpus that includes both handwritten and computer-generated scripts. The likelihood probability of the input pattern is calculated against each word model and the pattern is assigned to the model with the highest probability.