A multi-classifier combination strategy for the recognition of handwritten cursive words

The paper describes a recognition scheme for reading handwritten cursive words using three word recognition techniques. It particularly focuses on the implementation used to combine the three techniques based on a comparative sru& of different strategies. The first holistic recognition technique derives a global encoding of the word. The other techniques both rely on the segmentatiorr of the word into letters, bur diger in the character ClassiJier they use. The former runs a statistical linear classifier, and the latter runs a neural network with a different representation of the input data. The testing, comparison, and combination studies have been perfornied on word images from mail provided by the USPS. The top choice recognition rates achieved so far correspond to 88 %, 76 %, 6.5 % with respect to lexicon sizes of 10, 100, and IO00 words.