Increasing the effectiveness of handwritten Manipuri Meetei-Mayek character recognition using multiple-HOG-feature descriptors

Detection and reading of the text from natural images is a difficult computer vision task, which is essential in a variety of emerging applications. Document character recognition is one such problem, which has been widely studied and documented by many machine learning and computer vision researchers, which is practically used for solving applications like recognizing handwritten digits. In this paper, a new approach for efficiently extracting cognition out of a total of 56 different classes of Handwritten Manipuri Meetei-Mayek (HMMM) (an Indian language) is described. Although character recognition algorithms have been researched and developed for other Indian scripts, no research work has been reported so far for recognizing all the characters of the Manipuri Meetei-Mayek (MMM). The work begins with a thorough analysis of the recognition task using a single hidden layer type Multilayer Perceptron Feedforward Artificial Neural Network with Histogram of Oriented Gradient (HOG) feature descriptors. After reviewing the level of accuracy and time it takes to train the network, the limitations are experimentally removed using multiple-sized cell grids using HOG descriptors. HOG, being a gradient-based descriptor, is very efficient in data discrimination and very stable with illumination variation. For efficient classification of the HOG features of the MMM, a linear multiclass support vector machine (SVM) classifier has been proposed for classifying the different offline characters because of its simplicity and speed. The classification based on linear multiclass SVM yielded a very high overall accuracy of 96.928%