We present a system for recognizing off-line, cursive, English text, guided in part by global characteristics (style) of the handwriting. We introduce a new method for segmenting words into letters, based on minimizing a cost function. Segmented letters are normalized with a novel algorithm that scales different parts of a letter separately removing much of the variation in the writing. We use a neural network for letter recognition and use the output of the network as posterior probabilities of letters in the word recognition process. We found that using a hidden Markov Model for word recognition is less successful than assuming an independent process for our small set of test words. In our experiments with several hundred words, written by 7 writers, 96% of the test words were correctly segmented, 52% were correctly recognized, and 70% were in the top three choices.<>
Recognizing off-line cursive handwriting
01.01.1994
610477 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Recognizing Off-Line Cursive Handwriting
British Library Conference Proceedings | 1994
|Off-line cursive handwriting segmentation
IEEE | 1995
|Off-Line Cursive Handwriting Segmentation
British Library Conference Proceedings | 1995
|Recognition of Off-Line Cursive Handwriting
British Library Online Contents | 1998
|Dynamical encoding of cursive handwriting
IEEE | 1993
|