Abstract
Handwritten digits recognition has been an interesting area due to its applications in several fields. Recognition of bank account numbers and zip codes are a few examples. Handwritten digits recognition is not a trivial task due to presence of large variation in writing style in available data. In order to cope with this problem both features and classifier need to be efficient. In this research, transformation based features, Discrete Cosine Transform (2D-DCT), have been used. Hidden Markov models (HMMs) have been applied as classifier. The proposed algorithm has been trained and tested on Mixed National Institute of Standards and Technology (MNIST) handwritten digits database. The algorithm provides promising recognition results on MNIST database of handwritten digits.
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