Recognition of handwritten characters using local gradient feature descriptors

In this paper we propose to use local gradient feature descriptors, namely the scale invariant feature transform keypoint descriptor and the histogram of oriented gradients, for handwritten character recognition. The local gradient feature descriptors are used to extract feature vectors from the handwritten images, which are then presented to a machine learning algorithm to do the actual classification. As classifiers, the k-nearest neighbor and the support vector machine algorithms are used. We have evaluated these feature descriptors and classifiers on three different language scripts, namely Thai, Bangla, and Latin, consisting of both handwritten characters and digits. The results show that the local gradient feature descriptors significantly outperform directly using pixel intensities from the images. When the proposed feature descriptors are combined with the support vector machine, very high accuracies are obtained on the Thai handwritten datasets (character and digit), the Latin handwritten datasets (character and digit), and the Bangla handwritten digit dataset. HighlightsThis paper provides a new standard Thai handwritten character dataset for comparison of feature extraction techniques and methods.This paper propose to use local gradient feature descriptors for handwritten character recognition.This paper makes use of three complex datasets, namely Bangla, Thai, and Latin, for which very high recognition accuracies have not been obtained before.