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
More filters
Journal ArticleDOI
Handwritten character recognition using wavelet energy and extreme learning machine
TL;DR: An extremely fast leaning algorithm called ELM for single hidden layer feed forward networks (SLFN), which randomly chooses the input weights and analytically determines the output weights of SLFN, which learns much faster than traditional popular learning algorithms for feed forward neural networks.
Journal ArticleDOI
Offline Recognition of Devanagari Script: A Survey
TL;DR: In this paper, the state of the art from 1970s of machine printed and handwritten Devanagari optical character recognition (OCR) is discussed in various sections of the paper.
Journal ArticleDOI
Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)
TL;DR: This review article serves the purpose of presenting state of the art results and techniques on OCR and also provide research directions by highlighting research gaps.
Journal ArticleDOI
Diagonal Based Feature Extraction for Handwritten Alphabets Recognition System using Neural Network
TL;DR: An off-line handwritten alphabetical character recognition system using multilayer feed forward neural network that will be suitable for converting handwritten documents into structural text form and recognizing handwritten names is described in the paper.
Journal ArticleDOI
Multilingual scene character recognition with co-occurrence of histogram of oriented gradients
Shangxuan Tian,Ujjwal Bhattacharya,Shijian Lu,Bolan Su,Qingqing Wang,Xiaohua Wei,Yue Lu,Chew Lim Tan +7 more
TL;DR: The Histogram of Oriented Gradient is extended and two new feature descriptors are proposed: Co-occurrence HOG (Co-HOG) and Convolutional Co-Hog (ConvCo- HOG) for accurate recognition of scene texts of different languages.
References
More filters
Journal ArticleDOI
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.
Book
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.
Journal ArticleDOI
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.
Book ChapterDOI
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.