EXTRACTION OF KEY LETTERS FOR CURSIVE SCRIPT RECOGNITION

This paper presents a new method for cursive script recognition based on the extraction of key letters which consist of the parts of the handwritten text which are singularities of handwriting. By processing the raw input data representing the handwritten text, the singular features: loops, ascenders, and descenders are first defined and determined. Based on them, the key letters are then extracted. This paper focuses on the extraction of potentially meaningful shapes, corresponding to visual letters, in the recognition of cursive handwriting. First the key letters of a cursive word are found, such as, b, f, p, q, F, T, etc., then a possible word based on these letters is selected. In the testing phase, there are 304 words from 74 cheques in our data base written by 40 writers. 276 words are correctly segmented, that is more than 90% successful segmentation. The problems leading to the missegmentation are identified for further improvement.