Offline handwritten word recognition using MQDF-HMMs

Sitaram Ramachandrula; Mangesh Hambarde; Ajay Patial; Dushyant Sahoo; Shaivi Kochar

doi:10.1117/12.2076144

8 February 2015 Offline handwritten word recognition using MQDF-HMMs

Sitaram Ramachandrula, Mangesh Hambarde, Ajay Patial, Dushyant Sahoo, Shaivi Kochar

Proceedings Volume 9402, Document Recognition and Retrieval XXII; 94020J (2015) https://doi.org/10.1117/12.2076144
Event: SPIE/IS&T Electronic Imaging, 2015, San Francisco, California, United States

Abstract

We propose an improved HMM formulation for offline handwriting recognition (HWR). The main contribution of this work is using modified quadratic discriminant function (MQDF) [1] within HMM framework. In an MQDF-HMM the state observation likelihood is calculated by a weighted combination of MQDF likelihoods of individual Gaussians of GMM (Gaussian Mixture Model). The quadratic discriminant function (QDF) of a multivariate Gaussian can be rewritten by avoiding the inverse of covariance matrix by using the Eigen values and Eigen vectors of it. The MQDF is derived from QDF by substituting few of badly estimated lower-most Eigen values by an appropriate constant. The estimation errors of non-dominant Eigen vectors and Eigen values of covariance matrix for which the training data is insufficient can be controlled by this approach. MQDF has been successfully shown to improve the character recognition performance [1]. The usage of MQDF in HMM improves the computation, storage and modeling power of HMM when there is limited training data. We have got encouraging results on offline handwritten character (NIST database) and word recognition in English using MQDF HMMs.

Citation Download Citation

Sitaram Ramachandrula, Mangesh Hambarde, Ajay Patial, Dushyant Sahoo, and Shaivi Kochar "Offline handwritten word recognition using MQDF-HMMs", Proc. SPIE 9402, Document Recognition and Retrieval XXII, 94020J (8 February 2015); https://doi.org/10.1117/12.2076144

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