Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
Survey and bibliography of Arabic optical text recognition
Signal Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Survey of Methods and Strategies in Character Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Offline Recognition of Chinese Handwriting by Multifeature and Multilevel Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Character Recognition Without Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Experimental HMM-Based Postal OCR System
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 4 - Volume 4
Off-line recognition of handwritten Korean and alphanumeric characters using hidden Markov models
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 2) - Volume 2
A Full English Sentence Database for Off-Line Handwriting Recognition
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Advances in the BBN BYBLOS OCR System
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Modelling polyfont printed characters with HMMs and a shift invariant Hamming distance
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
Script-Independent, HMM-Based Text Line Finding for OCR
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Offline Recognition of Unconstrained Handwritten Texts Using HMMs and Statistical Language Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Global Shape Normalization for Handwritten Chinese Character Recognition: A New Method
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Arabic Handwriting Recognition Competition
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
The forward-backward search algorithm
ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference
Printed PAW Recognition Based on Planar Hidden Markov Models
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Modeling and recognition of cursive words with hidden Markov models
Pattern Recognition
Robust named entity detection using an Arabic offline handwriting recognition system
Proceedings of The Third Workshop on Analytics for Noisy Unstructured Text Data
A stroke regeneration method for cleaning rule-lines in handwritten document images
Proceedings of the International Workshop on Multilingual OCR
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Sparsity-based super-resolution for offline handwriting recognition
Proceedings of the 2011 Joint Workshop on Multilingual OCR and Analytics for Noisy Unstructured Text Data
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
Neural network language models for off-line handwriting recognition
Pattern Recognition
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This paper introduces a script-independent methodology for multilingual offline handwriting recognition (OHR) based on the use of Hidden Markov Models (HMM). The OHR methodology extends our script-independent approach for OCR of machine-printed text images. The feature extraction, training, and recognition components of the system are all designed to be script independent. The HMM training and recognition components are based on our Byblos continuous speech recognition system. The HMM parameters are estimated automatically from the training data, without the need for laborious hand-written rules. The system does not require pre-segmentation of the data, neither at the word level nor at the character level. Thus, the system can handle languages with cursive handwritten scripts in a straightforward manner. The script independence of the system is demonstrated with experimental results in three scripts that exhibit significant differences in glyph characteristics: English, Chinese, and Arabic. Results from an initial set of experiments are presented to demonstrate the viability of the proposed methodology.