Off-Line Cursive Script Word Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Communications of the ACM
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ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: image and multidimensional signal processing - Volume V
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ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: image and multidimensional signal processing - Volume V
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
An HMM Based Two-Pass Approach for Off-Line Cursive Handwriting Recognition
ICMI '00 Proceedings of the Third International Conference on Advances in Multimodal Interfaces
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ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
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BNCOD'07 Proceedings of the 24th British national conference on Databases
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Off-line cursive script recognition: current advances, comparisons and remaining problems
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Expert Systems with Applications: An International Journal
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In this paper, a new method for modeling and recognizing cursive words with hidden Markov models (HMM) is presented. In the proposed method, a sequence of thin fixed-width vertical frames are extracted from the image, capturing the local features of the handwriting. By quantizing the feature vectors of each frame, the input word image is represented as a Markov chain of discrete symbols. A handwritten word is regarded as a sequence of characters and optional ligatures. Hence, the ligatures are also explicitly modeled. With this view, an interconnection network of character and ligature HMMs is constructed to model words of indefinite length. This model can ideally describe any form of handwritten words, including discretely spaced words, pure cursive words and unconstrained words of mixed styles. Experiments have been conducted with a standard database to evaluate the performance of the overall scheme. The performance of various search strategies based on the forward and backward score has been compared. Experiments on the use of a preclassifier based on global features show that this approach may be useful for even large-vocabulary recognition tasks.