Unconstrained word-based approach for off-line script recognition using density-based random-vector functional-link net ☆

Abstract This paper reports on the performance of a holistic-styled word-based approach to off-line recognition of English language script. Neural-net computing is used in the implementation of the approach. The thrust of the investigation is straightforward. The objective of the work is to determine to what extent it is possible to build a ‘filter’ which would recognize a word holistically without segmentation, regardless of how ill formed it might be. Of interest also are the failure modes. This investigation required the introduction of innovation in neural-net computing consisting principally of combining the practices of radial basis function neural net (RBNN) approach and the random-vector functional-link net approach (RVFLN). The combined method is called the density-based random-vector functional-link net (DBRVFLN) and it is helpful in improving the performance of the word recognition filter. The CEDAR database was used for training and validation. That database is a real-world database and we used the script images written by 2490 anonymous writers. In-depth investigation on one word yielded results of a very low substitution error rate of 0.74%, with a rejection rate of 11.48%.