Handwritten Numeral Databases of Indian Scripts and Multistage Recognition of Mixed Numerals
01 Mar 2009-IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE Computer Society)-Vol. 31, Iss: 3, pp 444-457
TL;DR: 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.
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TL;DR: Experimental results show that Linear, Quadratic and Mahalanobis discriminant functions provide better results and Combination classifier offers better results over individual classifiers.
Abstract: This paper presents a Devnagari Numerical recognition method based on statistical discriminant functions. 17 geometric features based on pixel connectivity, lines, line directions, holes, image area, perimeter, eccentricity, solidity, orientation etc. are used for representing the numerals. Five discriminant functions viz. Linear, Quadratic, Diaglinear, Diagquadratic and Mahalanobis distance are used for classification. 1500 handwritten numerals are used for training. Another 1500 handwritten numerals are used for testing. Experimental results show that Linear, Quadratic and Mahalanobis discriminant functions provide better results. Results of these three Discriminants are fed to a majority voting type Combination classifier. It is found that Combination classifier offers better results over individual classifiers.
20 citations
TL;DR: A novel benchmark performance is conceived that has delivered state-of-the-art decisions on two regional handwritten character identifications and the mathematical rationale for using non-linearity in the deep learning (DL) model is stretched.
Abstract: Recognition of handwritten characters in two Indic scripts Bangla and Meitei Mayek is one of the challenging responsibilities due to intricate patterns and scarcity of standard datasets. Convolutional Neural Network (CNN) is one of the stablest well-known techniques for classifying objects in distinctive specialties as it has an extraordinary capability of discovering complex patterns. In this paper, we hook a different layout and obtain a unique CNN architecture from scratch, which has manifold advantages over classical machine learning (ML) approaches, and it has a unique ability to consolidate feature extraction and classification altogether. Further, we stretch our work to uncover the mathematical rationale for using non-linearity in the deep learning (DL) model. Our proposed CNN architecture consists of four layers, including convolutional layer (CL), nonlinear activation layer (AL), pooling layer (PL), and fully connected layer (FCL), which are used in the existing two accessible Bangla datasets named cMATERdb and ISI Bangla datasets. The identical model also validates on proposed Manipuri Character dataset, called “Mayek27”. Moreover, we perform an in-depth comparison with different batch sizes and optimization techniques over all the datasets for understanding their functionality. We conceive a novel benchmark performance that has delivered state-of-the-art decisions on two regional handwritten character identifications.
20 citations
TL;DR: A novel framework based on improved particle swarm optimization (PSO) algorithm to automatically construct optimal convolutional neural network (CNN) architecture has been proposed with an aim to outperform the existing techniques.
19 citations
TL;DR: A modification of previously proposed CNN architecture has given an accuracy of 98.91% and the proposed architecture has produced 99.76%, which is comparable to state-of-the-art results found in the domain of handwritten Arabic numeral recognition.
Abstract: Numeral recognition is an important preliminary step for optical character recognition, document understanding and others. Deep learning based numeral recognition models have achieved huge popularity among the researchers around the world since last few years. Several convolutional neural network (CNN) based architectures have been proposed and many of those have achieved state-of-the-art results in numeral recognition. In this paper, we have explored CNN based architectures for handwritten Arabic numeral recognition. We have also developed a handwritten Arabic numerals dataset using various morphological operations on an existing dataset thus increasing the size of the dataset from 3000 to 72,000 images. A modification of previously proposed CNN architecture has given us an accuracy of 98.91% and our proposed architecture has produced 99.76%, which is comparable to state-of-the-art results found in the domain of handwritten Arabic numeral recognition.
19 citations
TL;DR: The results show that the proposed system yields good recognition rates which are comparable to that of feature extraction based schemes for handwritten character recognition.
Abstract: Handwritten character recognition has been one of the active and challenging research areas in the field of image processing and pattern recognition. It has numerous applications which include, reading aid for blind, bank cheques and conversion of any hand written document into structural text form. In this paper an attempt is made to recognize handwritten characters for English alphabets without feature extraction using multilayer Feed Forward neural network. Each character data set contains 26 alphabets. Fifty different character data sets are used for training the neural network. The trained network is used for classification and recognition. In the proposed system, each character is resized into 30x20 pixels, which is directly subjected to training. That is, each resized character has 600 pixels and these pixels are taken as features for training the neural network. The results show that the proposed system yields good recognition rates which are comparable to that of feature extraction based schemes for handwritten character recognition
19 citations
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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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.
Abstract: From the Publisher:
This book represents the most comprehensive treatment available of neural networks from an engineering perspective. Thorough, well-organized, and completely up to date, it 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. Written in a concise and fluid manner, by a foremost engineering textbook author, to make the material more accessible, this book is ideal for professional engineers and graduate students entering this exciting field. Computer experiments, problems, worked examples, a bibliography, photographs, and illustrations reinforce key concepts.
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TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.
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