Low resolution, degraded document recognition using neural networks and hidden Markov models
We collected a large, real world database, containing degraded, old and faxed documents and present a comparison between two leading edge commercial software packages and human reading performance which shows quantitatively the huge performance gap between humans and machines, even on random character documents where no context can be used. This indicates room for possible improvements. We implemented an integrated segmentation and recognition algorithm using neural networks and hidden Markov models trained on the database and present results which show the superior performance of the algorithm.
low resolution document recognition neural networks hidden markov models ocr