Use of adaptive segmentation in handwritten phrase recognition

Abstract

Research in handwriting recognition has thus far been primarily focused on recognizing words and phrases. In fact, phrases are usually treated as a concatenation of the constituent words making it in essence an enhanced word recognizer. In this paper we present a methodology that will take advantage of the spacing between the words in a phrase to aid the recognition process. The novelty of our approach lies in the fact that the determination of word breaks is made in a manner that adapts to the writing style of the individual. The parameters that decide whether a particular gap between components is an inter-word gap or an inter-character gap are computed without the necessity of generalizing over a large training set. Rather, it is tuned to the distribution of the gaps within the instance of the phrase image being examined. We compare our approach to the methods described in the literature that simply ignore the significance of gaps in a phrase. Our experiments show an improvement of about 5% in recognition rates. On a test set of about 1400 phrase images the segmentation method “misses” only 2% of the true word break points.