Project Reading 6: Improving Offline Handwritten Text Recognition with Hybrid HMM/ANN Models

Citation

Espana-Boquera, Salvador, et al. "Improving offline handwritten text recognition with hybrid HMM/ANN models." Pattern Analysis and Machine Intelligence, IEEE Transactions on 33.4 (2011): 767-779.

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Summary

The paper presents a hybrid approach to optical character recognition. The idea is to use Hidden Markov Models (HMMs) to model the structural part of the optical input and use a multi layer perceptron to estimate the different classification probabilities. The paper also presents new methods to pre process the input images in terms of slope correction, size normalization and slant correction. The system was tested on IAM database.

Discussion

The image shows the hybrid Hidden Markov Model and Artificial Neural Networks technique used in this paper. First the images are pre-processed and resulting feature vector and a contextual vector from left and right of the character is processed by a Multi Layer Perceptron (MLP). The MLP outputs are then used as emission probabilities in HMMs.