An HMM-based approach for off-line unconstrained handwritten word modelingand recognition

This paper describes a hidden Markov model-based approach designed to recog nize off-line unconstrained handwritten words for large vocabularies. After preprocessing, a word image is segmented into letters or pseudoletters and represented by two feature sequences of equal length, each consisting of a n alternating sequence of shape-symbols and segmentation-symbols, which are both explicitly modeled. The word model is made up of the concatenation of appropriate letter models consisting of elementary HMMs and an HMM-based i nterpolation technique is used to optimally combine the two feature sets. T wo rejection mechanisms are considered depending on whether or not the word image is guaranteed to belong to the lexicon. Experiments carried out on r eal-life data show that the proposed approach can be successfully used for handwritten word recognition.