Recognition of Handwritten Phrases as Applied to Street Name Images

Abstract

A method for recognition of street name phrases collected from mail pieces is presented in this paper. Some of the challenges posed by the problem are: (i) patron errors, (ii) non-standardized way of abbreviating names, and (iii) variable number of words in a street name image. A neural network has been designed to segment words in a phrase, a street name in this case, using distances between components and style of writing. The network learns the type of spacing (including size) that one should expect between different pairs of characters in handwritten test. Experiments show perfect word segmentation performance at about 85% of cases. Unlike conventional methods, where lexicon entries are expanded to take care of all variations of prefixes and sizes, substring matching is attempted only between the main body of a lexicon entry and the word segments of an image. Efforts to reduce computational complexity are successfully made by the sharing of character segmentation results between the segmentation and recognition phases. 83% phrase recognition accuracy is achieved on a test set.