Initial Letter Spotting as a Complementary Feature for Lexical Filtering of Cursive Words

摘要

This paper describes a method for lexical filtering to be employed mainly (but not exclusively) in cursive word recognition. This method operates at word level by selecting a subset of the original vocabulary based on a simple and robust characterization of the image of the unknown word. The features adopted for word representation are basically morphological (ascenders, descenders, loops) or structural (hypothesized number of letters). In this work we have added another feature to improve the efficiency of the lexical filter, namely the recognition of the initial letter. In order to classify the initial letter reliably, without segmentation or ad-hoc training, we applied an MLP-based character spotting method.