Cursive script word recognition is the problem of transforming a word from the iconic form of cursive writing to its symbolic form. Several component processes of a recognition system for isolated offline cursive script words are described. A word image is transformed through a hierarchy of representation levels: points, contours, features, letters, and words. A unique feature representation is generated bottom-up from the image using statistical dependences between letters and features. Ratings for partially formed words are computed using a stack algorithm and a lexicon represented as a trie. Several novel techniques for low- and intermediate-level processing for cursive script are described, including heuristics for reference line finding, letter segmentation based on detecting local minima along the lower contour and areas with low vertical profiles, simultaneous encoding of contours and their topological relationships, extracting features, and finding shape-oriented events. Experiments demonstrating the performance of the system are also described.>

This paper deals with a method of estimating a correct string X from its noisy version Y produced by a cursive script recognition system. An accurate channel model that allows for splitting, merging, and substitution of symbols is introduced. The best estimate X is obtained by using a dynamic programming search which combines a known search strategy (stack decoding) with a trie structure representation of a dictionary. The computational complexity of the algorithm is derived and compared with that of a method based on the generalized Levenshtein metric. Experimental results with the algorithm on English text based on a dictionary of the 1027 most commonly occurring words are described.