Journal ArticleDOI
Handwritten Numeral Databases of Indian Scripts and Multistage Recognition of Mixed Numerals
TLDR
P pioneering development of two databases for handwritten numerals of two most popular Indian scripts, a multistage cascaded recognition scheme using wavelet based multiresolution representations and multilayer perceptron classifiers and application for the recognition of mixed handwritten numeral recognition of three Indian scripts Devanagari, Bangla and English.Abstract:
This article primarily concerns the problem of isolated handwritten numeral recognition of major Indian scripts. The principal contributions presented here are (a) pioneering development of two databases for handwritten numerals of two most popular Indian scripts, (b) a multistage cascaded recognition scheme using wavelet based multiresolution representations and multilayer perceptron classifiers and (c) application of (b) for the recognition of mixed handwritten numerals of three Indian scripts Devanagari, Bangla and English. The present databases include respectively 22,556 and 23,392 handwritten isolated numeral samples of Devanagari and Bangla collected from real-life situations and these can be made available free of cost to researchers of other academic Institutions. In the proposed scheme, a numeral is subjected to three multilayer perceptron classifiers corresponding to three coarse-to-fine resolution levels in a cascaded manner. If rejection occurred even at the highest resolution, another multilayer perceptron is used as the final attempt to recognize the input numeral by combining the outputs of three classifiers of the previous stages. This scheme has been extended to the situation when the script of a document is not known a priori or the numerals written on a document belong to different scripts. Handwritten numerals in mixed scripts are frequently found in Indian postal mails and table-form documents.read more
Citations
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Journal ArticleDOI
Offline Handwritten Devanagari Numeral Recognition Using Artificial Neural Network
TL;DR: This research work proposes new approaches for extracting features in context of Handwritten Marathi numeral recognition by using Artificial Network for classification technique Artificial Network.
Dissertation
Handwritten Character Recognition of a Vernacular Language: The Odia Script
TL;DR: This thesis has designed databases on handwritten Odia Digit, and character for the simulation of the proposed schemes, and a multi-resolution scheme has been suggested to extract features from Odia atomic character and recognize them using the back propagation neural network.
Symbolic and Deep Learning Based Data Representation Methods for Activity Recognition and Image Understanding at Pixel Level
TL;DR: A chronology of key events and quotes from the 12-month campaign to elect US President Barack Obama in the 2016 election can be found at www.score.gov/campaigns.
Book ChapterDOI
A Robust Approach with Text Analytics for Bengali Digit Recognition Using Machine Learning
TL;DR: The Bengali numerals obtained from the NumtaDB dataset with the implementation of a convolutional neural network (CNN) are classified using the same classification technique and a whopping accuracy of 98.40%.
Journal ArticleDOI
Off-Line English Character Recognition with Geometric Discretization
TL;DR: This study is going to focus on the Off-Line English language characters in order to extract geometric moment’s features for the Characters shape of the Handwriting recognition to reduce the similarity error for intra-class (of the same character), with the increase of the similarityerror for inter- class (of different characters) in recognition of Off- line handwritten English characters with Fuzzy logic.
References
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Learning internal representations by error propagation
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