Statistical methods for speech recognition
Statistical methods for speech recognition
An Omnifont Open-Vocabulary OCR System for English and Arabic
IEEE Transactions on Pattern Analysis and Machine Intelligence
Offline Recognition of Syntax-Constrained Cursive Handwritten Text
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Spontaneous Handwriting Recognition and Classification
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
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Finite-state models are used to implement a handwritten text recognition and classification system for a real application entailing casual, spontaneous writing with large vocabulary. Handwritten short phrases which involve a wide variety of writing styles and contain many non-textual artifacts, are to be classified into a small number of predefined classes. To this end, two different types of statistical framework for phrase recognition-classification are considered, based on finite-state models. HMMs are used for text recognition process. Depending to the considered architecture, N-grams are used for performing text recognition and then text classification (serial approach) or for performing both simultaneously (integrated approach). The multinomial text classifier is also employed in the classification phase of the serial approach. Experimental results are reported which, given the extreme difficulty of the task, are encouraging.