Unconstrained Offline Handwriting Recognition using Connectionist Character N-grams

This work presents an unconstrained offline handwritten line recognition system based on hybrid HMM (Hidden Markov Model)/ANN (Artificial Neural Network) models. The particularity of the system lies in the use of an ensemble of connectionist/statistical character n-gram language models. These language models are trained with a text corpus at character level; therefore, no explicit lexicon is used during recognition. The recognizer is thus able to output words which do not belong to that corpus. The proposed system favorably behaves compared to using a standard character n-gram on the IAM database lines corpus and achieves error rates comparable to state-of-the-art lexicon-driven alternatives.