Abstract: Recognition of general unconstrained cursive handwriting remains largely unsolved. We present a system for recognizing off-line cursive English text guided in part by global characteristics of the handwriting. A new method for finding the letter boundaries based on minimizing a heuristic cost function is introduced. The function is evaluated at each point along the baseline of the word to find the best possible segmentation points. The algorithm tries to find all the actual letter boundaries and as few additional ones as possible. After a normalization step that removes much of the style variation, the normalized segments are classified by a one hidden layer feedforward neural network. The word recognition algorithms find the segmentation points that are likely to be extraneous and generates all possible final segmentations of the word by either keeping or removing them. Interpreting the output of the neural network as posterior probabilities of letters, it then finds the word that maximizes the probability of having produced the image, over a set of 30,000 words and over all the possible final segmentations. We compared two hypotheses for finding the likelihood of words that are in the lexicon and found that using a Hidden Markov Model of English is significantly less successful than assuming independence among the letters of a word. In our initial test involving multiple writers, 68% of the words were in the top three choices.!22
展开▼
