A new technique for classifying all 56 different characters of the Manipuri Meetei-Mayek (MMM) is proposed herein. The characters are grouped under five categories, which are Eeyek Eepee (original alphabets), Lom Eeyek (additional letters), Cheising Eeyek (digits), Lonsum Eeyek (letters with short endings), and Cheitap Eeyek (vowel signs. Two related works proposed by previous researchers are studied for understanding the benefits claimed by the proposed deep learning approach in handwritten Manipuri Meetei-Mayek. (1) Histogram of Oriented (HOG) with SVM classifier is implemented for thoroughly understanding how HOG features can influence accuracy. (2) The handwritten samples are trained using simple Convolutional Neural Network (CNN) and compared with the proposed CNN-based architecture. Significant progress has been made in the field of Optical Character Recognition (OCR) for well-known Indian languages as well as globally popular languages. Our work is novel in the sense that there is no record of work available to date that is able to classify all 56 classes of the MMM. It will also serve as a pre-cursor for developing end-to-end OCR software for translating old manuscripts, newspaper archives, books, and so on.
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% Keywords. Manipuri Meetei-Mayek (script); multilayer perceptron; feedforward artificial neural network; histogram of oriented gradient (HOG); linear multiclass support vector machine (SVM).
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.