Optical Character Recognition for Cursive Handwriting
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
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
Arabic Handwriting Recognition Competition
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
Pattern Recognition Letters
Recognition of off-line printed Arabic text using Hidden Markov Models
Signal Processing
Expert Systems with Applications: An International Journal
Off-line handwritten word recognition using multi-stream hidden Markov models
Pattern Recognition Letters
Databases and competitions: strategies to improve Arabic recognition systems
SACH'06 Proceedings of the 2006 conference on Arabic and Chinese handwriting recognition
Multi-character field recognition for Arabic and Chinese handwriting
SACH'06 Proceedings of the 2006 conference on Arabic and Chinese handwriting recognition
Gabor features for offline Arabic handwriting recognition
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
Offline handwritten Arabic cursive text recognition using Hidden Markov Models and re-ranking
Pattern Recognition Letters
Offline arabic handwritten text recognition: A Survey
ACM Computing Surveys (CSUR)
Multilingual OCR research and applications: an overview
Proceedings of the 4th International Workshop on Multilingual OCR
KHATT: An open Arabic offline handwritten text database
Pattern Recognition
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In this paper we describe a 1D HMM off-line handwriting recognition system employing an analytical approach. The system is supported by a set of robust language independent features extracted on binary images. Parameters such as lower and upper baselines are used to derive a subset of baseline dependent features. Thus, word variability due to lower and upper parts of words is better taken into account. In addition, the proposed system learns character models without character pre-segmentation. Experiments that have been conducted on the benchmark IFN/ENIT database of Tunisian handwritten country/village names, show the advantage of the proposed approach and of the baseline- dependant features.