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
BDNet: Bengali Handwritten Numeral Digit Recognition based on Densely connected Convolutional Neural Networks
TL;DR: In this article, a densely connected deep convolutional neural network model was used to classify (recognize) Bengali handwritten numeral digits, which achieved the test accuracy of 99.775% on the test dataset of ISI Bengali numerals.
Proceedings ArticleDOI
Script independent feature set for handwritten text recognition
Deepti Khanduja,Neeta Nain +1 more
TL;DR: This paper proposes a hybrid approach combining the structural features of the character and a mathematical model of curve fitting to simulate the best features of a character to achieve script independent feature representation.
Proceedings ArticleDOI
Automatic extraction of numeric strings in unconstrained handwritten document images
TL;DR: A novel algorithm for automatic extraction of numeric strings in unconstrained handwritten document images using probabilistic RBF networks for real-world documents where letters and digits may be connected or broken in a document.
Proceedings ArticleDOI
Comparison of the classifiers in Bangla handwritten numeral recognition
TL;DR: This paper has presented the detailed comparison of classifiers for Bangla handwritten numeral recognition and LIBLINEAR is found to be the fastest in terms of convergence criteria while MQDF outperform others interms of recognition result for the authors' WBSUCS character database.
Proceedings ArticleDOI
Optical Character Recognition Techniques: A Review
TL;DR: In this paper , an exhaustive research is going on character recognition of different languages including English and Devanagiri in India, which is an eminent topic of research in modern times and various algorithms are being developed now days for increasing the reliability of these characters for accurate recognition.
References
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TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
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Learning internal representations by error propagation
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