Bootstrapping text recognition from stop words
Tin Kam Ho · 2002
Recognition of arbitrary noisy English text has been difficult because of problems in character segmentation and multi-font symbol classification. Both segmentation and recognition can be easier with more knowledge of the dominant font used in a given text page. This has led to some recent studies that show promising methods for extracting character prototypes from a text image provided that truth is given for part of the image. In this paper we investigate the feasibility of such a strategy without dependence on ground truth. We replace the needed truth by results of direct recognition of some frequently occurring words. The method makes use of the observation that over half of the words in a typical English text passage are contained in a very small lexicon.