Abstract—A fast method of handwritten word recognition suitable for real time applications is presented in this paper. Preprocessing, segmentation and feature extraction are implemented using a chain code representation of the word contour. Dynamic matching between characters of a lexicon entry and segment(s) of the input word image is used to rank the lexicon entries in order of best match. Variable duration for each character is defined and used during the matching. Experimental results prove that our approach using the variable duration outperforms the method using fixed duration in terms of both accuracy and speed. Speed of the entire recognition process is about 200 msec on a single SPARC-10 platform and the recognition accuracy is 96.8 percent are achieved for lexicon size of 10, on a database of postal words captured at 212 dpi. Index Terms—Handwritten word recognition, segmentation algorithm, variable duration, chain code representation, dynamic

The design and functionality of a versatile search engine on handwritten documents is described. Documents are indexed using global image features, e.g., stroke width, slant, word gaps, as well local features that describe shapes of characters and words. Image indexing is done automatically using page analysis, page segmentation, line separation, word segmentation and recognition of characters and words. Several types of searches are

The general problem of decoding an acoustic sequence into an optimal word string under the probabilistic framework cannot be solved by an algorithm in polynomial time. This result is achieved by using the Computational Complexity Theory. The main consequence of this result is that in the most successful framework of speech recognition only suboptimal decoding can be achieved.