2014 12th International Conference on Frontiers of Information Technology (FIT) (2014)
Dec. 17, 2014 to Dec. 19, 2014
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/FIT.2014.63
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.
Hidden Markov models, Handwriting recognition, Feature extraction, Accuracy, Databases, Discrete cosine transforms, NIST,MNIST, digits recognition, handwritten, DCT, HMM
Syed Salman Ali, Muhammad Usman Ghani, "Handwritten Digit Recognition Using DCT and HMMs", 2014 12th International Conference on Frontiers of Information Technology (FIT), vol. 00, no. , pp. 303-306, 2014, doi:10.1109/FIT.2014.63