Contextual focus for improved recognition of hand-filled forms

The advances in Optical Character Recognition (OCR) technology over the past decade have enabled the development of many automatic document-processing systems capable of 99% correct recognition on printed text. However, similar advances in Cursive Script Recognition (CSR) technology have not been forthcoming due, principally, to the vast variability of human handwriting. This paper investigates a method by which the more reliable OCR technology can be used to improve the CSR performance in a form processing application. A novel method is proposed to link handwriting data to contextual cue words that have been automatically obtained from an OCR process. This information is then used to select appropriate ´focused´ lexicons to achieve better CSR results. The method was tested on 30 forms that were filled by 10 different writers. The experimental results together with a comparison to the base line recognition performance are presented