Fundamentals of speech recognition
Fundamentals of speech recognition
Arabic Handwriting Recognition Competition
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Embedded Bernoulli Mixture HMMs for Handwritten Word Recognition
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
ICDAR 2009 Arabic Handwriting Recognition Competition
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
ICDAR 2009 Handwriting Recognition Competition
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Results of the RIMES Evaluation Campaign for Handwritten Mail Processing
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
ICFHR 2010 - Arabic Handwriting Recognition Competition
ICFHR '10 Proceedings of the 2010 12th International Conference on Frontiers in Handwriting Recognition
Windowed Bernoulli Mixture HMMs for Arabic Handwritten Word Recognition
ICFHR '10 Proceedings of the 2010 12th International Conference on Frontiers in Handwriting Recognition
Dynamic and Contextual Information in HMM Modeling for Handwritten Word Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICDAR 2011 - Arabic Handwriting Recognition Competition
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
ICDAR 2011 - French Handwriting Recognition Competition
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
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Hidden Markov Models (HMMs) are now widely used for off-line handwriting recognition in many languages. As in speech recognition, they are usually built from shared, embedded HMMs at symbol level, where state-conditional probability density functions in each HMM are modeled with Gaussian mixtures. In contrast to speech recognition, however, it is unclear which kind of features should be used and, indeed, very different features sets are in use today. Among them, we have recently proposed to directly use columns of raw, binary image pixels, which are directly fed into embedded Bernoulli (mixture) HMMs, that is, embedded HMMs in which the emission probabilities are modeled with Bernoulli mixtures. The idea is to by-pass feature extraction and to ensure that no discriminative information is filtered out during feature extraction, which in some sense is integrated into the recognition model. In this work, column bit vectors are extended by means of a sliding window of adequate width to better capture image context at each horizontal position of the word image. Using these windowed Bernoulli mixture HMMs, good results are reported on the well-known IAM and RIMES databases of Latin script, and in particular, state-of-the-art results are provided on the IfN/ENIT database of Arabic handwritten words.