Suprabhat Maity

Bio: Suprabhat Maity is an academic researcher from B. P. Poddar Institute of Management & Technology. The author has contributed to research in topics: Word recognition & Bengali. The author has an hindex of 1, co-authored 1 publications receiving 2 citations.

Handwritten Bengali Character Recognition Using Deep Convolution Neural Network

Abstract: Recognition of Handwritten Character had been one of the promising area of research for its applications in diverse field, it appear to be a challenging research In our paper, we focus specifically on offline handwritten character recognition of regional language (Bengali) by first detecting individual characters The principal approaches for offline handwritten character recognition may be divided into two classes, segmentation and holistic based In our method we applied segmentation based handwritten word recognition and to identify individual characters neural network have been used We have used 15,000 instances of Bengali alphabets to create a recognition model, which when provided with images of physical pieces of handwritten texts, it is able to segment and extract characters from the said image of a physical handwritten text with 65% accuracy, and recognize the properly segmented alphabets with 995% accuracy

Quantitative Analysis of Deep CNNs for Multilingual Handwritten Digit Recognition

Abstract: Indian subcontinent is a birthplace of multilingual people, where documents such as job application form, passport, number plate identification, and so forth are composed of text contents written in different languages or scripts. These scripts consist of different Indic numerals in a single document page. Recently, deep convolutional neural networks (CNN) have achieved favorable result in computer vision problems, especially in recognizing handwritten digits but most of the works focuses on only one language, i.e., English or Hindi or Bangla, etc. However, developing a language-invariant method is very important as we live in a global village now. In this work, we have examined the performance of the ten state-of-the-art deep CNN methods for the recognition of handwritten digits using four most common languages in the Indian sub-continent that creates the foundation of a script invariant handwritten digit recognition system. Among the deep CNNs, Inception-v4 performs the best based on accuracy and computation time. Besides, it discusses the limitations of existing techniques and shows future research directions.

An Approach to Pattern Recognition for Identification of Devnagari Script Based on Fingertips and Palm

Abstract: In this paper, we present an finger point based signed language symbol to text identification and classification algorithm that is based upon RGB image datasets. The palm sized images based upon different sizes, backgrounds, orientation are captured to be preprocessed as per the requirements of developing a convolution neural network based algorithm. This algorithm utilizes Alexnet for the preprocessing requisites where in 47 symbols of Devanagari script are augmented based on the reference rulebook created for our requirements as highlighted in the paper. At the primary level this algorithm provides an excellent classification rate which promises upliftment for our research in the upcoming future. We have provided detailed steps and discussion on the classification parameters considered for our algorithm which is implemented on MATLAB platform with the help of machine learning solution libraries.

Hog Features Based Handwritten Bengali Numerals Recognition Using SVM Classifier: A Comparison with Hopfield Implementation

Abstract: Handwritten digit recognition (reading by computer) is a process that gives the technological capabilities to a system to recognize handwritten numerals using Machine Learning. It is very tough to recognize handwritten characters because of the non-similarity in characters by different people. Also, there exists no standard technique available in the industry which can be applicable to provide innovative solutions to all kinds of handwritten characters. Hence, there is a requirement to understand the nature of the script. In order to complete the task, there is a need to use unique extraction methods and a classifier. This work implements a Histogram of Oriented Gradient (HOG) features with Support Vector Machine (SVM) classifier and a Hopfield model to recognize handwritten Bengali numerals into the class of ten segments. SVM works in the way of mapping the input elements to a set of the high-dimensional feature vector. This works both in the case of linearly and nonlinearly and separable data. The experiments have been done using 1200 handwritten digits for 10 different classes (0 to 9). The overall accuracy (productivity) obtained from the implementation is 89.5%. The experimental outcomes from the Hopfield model give an accuracy of 95.5%. Also, it is found the Hopfield model is more capable of handling noisy inputs.