Towards General Cursive Script Recognition

In this paper we present a system for the off-line recognition of hand-written sentences. The system takes scanned images of handwritten text as input. An input image is first binarized. Next, the lines are extracted. Then feature extraction is performed by a neural network. The extracted features are input to a hidden Markov model, where word recognition takes place. After word recognition contextual postprocessing is performed by means of a language model. Using a simple statistical bigram model, we could improve the recognition rate of the system from 74% to 85% on the word level.