Hidden Markov Models for Speech Recognition
Hidden Markov Models for Speech Recognition
An Enhanced HMM Topology in an LBA Framework for the Recognition of Handwritten Numeral Strings
ICAPR '01 Proceedings of the Second International Conference on Advances in Pattern Recognition
An implicit segmentation-based method for recognition of handwritten strings of characters
Proceedings of the 2006 ACM symposium on Applied computing
Off-line cursive script recognition: current advances, comparisons and remaining problems
Artificial Intelligence Review
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We apply an HMM-based text recognition system to the recognition of handwritten digit strings of unknown length. The algorithm is tailored to the input data by controlling the maximum number of levels searched by the Level Building (LB) search algorithm. We demonstrate that setting this parameter according to the pixel length of the observation sequence, rather than using a fixed value for all input data, results in a faster and more accurate system. Best results were achieved by setting the maximum number of levels to twice the estimated number of characters in the input string. We also describe experiments which show the potential for further improvement by using an adaptive termination criterion in the LB search.