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
A comprehensive survey of handwritten document benchmarks: structure, usage and evaluation
TL;DR: A comprehensive survey of the handwriting databases developed during the last two decades is presented in this article, where the ground truth information of the databases along with the supported tasks is also discussed.
Journal ArticleDOI
A Survey on Handwritten Character Recognition (HCR) Techniques for English Alphabets
Manoj Sonkusare,Narendra Sahu +1 more
TL;DR: An outline of current research work conducted for recognition of handwritten English alphabets is presented and a variety of recognition methodologies are conferred here alongside with their performance.
Journal ArticleDOI
Predicting Student Academic Performance: A Comparison of Two Meta-Heuristic Algorithms Inspired by Cuckoo Birds for Training Neural Networks
TL;DR: An approach to the problem based on the artificial neural network with the two meta-heuristic algorithms inspired by cuckoo birds and their lifestyle, namely, Cuckoo Search and Cuckoos Optimization Algorithm is proposed, demonstrating that both CS and COA have potential in training and slightly better results for predicting student academic performance.
Proceedings ArticleDOI
A hybrid deep model with HOG features for Bangla handwritten numeral classification
TL;DR: A hybrid model is presented, which aims to classify the Bengali numerals more precisely by bridges hand crafted feature extraction based approaches with the automatically learnt features of Convolutional Neural networks (CNN).
Proceedings ArticleDOI
Bangla Handwritten Digit Recognition Using Deep CNN for Large and Unbiased Dataset
TL;DR: The deep convolutional neural network model has shown an excellent performance, securing the 13th position with 92.72% testing accuracy in the Bengali handwritten digit recognition challenge 2018 among 57 participating teams.
References
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Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
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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Neural Networks: A Comprehensive Foundation
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
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A theory for multiresolution signal decomposition: the wavelet representation
TL;DR: In this paper, it is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2 /sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions.
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Learning internal representations by error propagation
TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.