scispace - formally typeset
Journal ArticleDOI

Handwritten Numeral Databases of Indian Scripts and Multistage Recognition of Mixed Numerals

Reads0
Chats0
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
Proceedings ArticleDOI

Analysis of Telugu Palm Leaf Character Recognition Using 3D Feature

TL;DR: This paper deals with the recognition of Telugu characters on palm leaf using statistical features and finds that the best recognition accuracy obtained is 96%.
Proceedings ArticleDOI

CNN based Optical Character Recognition and Applications

TL;DR: In this paper, CNN layers, architecture and implementation of CNN architecture are discussed and the CNN (VGG-16) model is trained over Telugu character data set which covers maximum of 1600 characters and its accuracy is measured.
Book ChapterDOI

Zone-based hybrid feature extraction algorithm for handwritten numeral recognition of four Indian scripts

TL;DR: A zone-based hybrid feature extraction system for multi-lingual and multi-script country India, where eighteen official scripts are accepted and there are over hundred regional languages is proposed.
Journal ArticleDOI

Recognition of Devanagari Handwritten Numerals using Gradient Features and SVM

TL;DR: The proposed system is based on the division of sample image into sub-blocks and then in each sub-block Strength of Gradient is accumulated in 8 standard directions in which Gradient Direction is decomposed resulting in a feature vector with dimensionality of 200.
Proceedings ArticleDOI

Robust transform learning

TL;DR: Experiments carried out on image analysis and impulse denoising elucidate the superiority of the robust transform learning method.
References
More filters
Journal ArticleDOI

Gradient-based learning applied to document recognition

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

Simon Haykin
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.
Related Papers (5)