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: Development of an algorithmic approach for grouping of various pattern classes needed for the second pass and a soft computing methodology for optimal selection of local regions of character images for extraction of features, specific to each grouping of pattern classes are presented.
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TL;DR: A simple, lightweight CNN model has been proposed in this paper for classifying Bangla Handwriting Character, which contains 50 basic Bangla characters, and achieved the best accuracy rate so far for BanglaLekha-Isolated, CMATERdb and ISI datasets.
53 citations
18 Mar 2011
TL;DR: An attempt is made to recognize handwritten characters for English alphabets without feature extraction using multilayer Feed Forward neural network, which yields good recognition rates which are comparable to that of feature extraction based schemes for handwritten character recognition.
Abstract: Handwriting 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 30×20 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.
53 citations
TL;DR: The most vital processes in script identification are addressed in detail: identification and discriminating methods, features extraction (local and global, and classification), and classification.
Abstract: In recent years, with the widespread of Internet and digitized processing of multi-script documents worldwide, script identification techniques have become more important in the pattern recognition field. Script identification concerns methods for identifying different scripts in multi-lingual, multi-script documents. This paper presents a comprehensive overview on research activities in the field and focuses on the most valuable results obtained so far. The most vital processes in script identification are addressed in detail: identification and discriminating methods, features extraction (local and global), and classification. Different kinds of approaches have been developed and promising results have been achieved. This paper reports SoA performance results. This paper reports methods concerning handwritten, printed, and hybrid document processing. More research is necessary to meet the performance levels essential for everyday applications.
49 citations
Cites background or methods from "Handwritten Numeral Databases of In..."
...Other classification methods were considered for script identification such as Neural Network [9], [96], quadratic classifier [43], [64], [75], [126], [134], rule-based classifiers [1], [90], [92], [93], Linear Discriminant Classifiers [43], [55], [83], Gaussian Mixture Model [11], [48], [50], [99], etc.....
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...The method mainly uses water reservoir concept based features, fractal-based features and a Neural Network classifier....
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...Transform (DCT) [92], Gray level co-occurrence matrix [9], [44], rotation invariant features [85], gradient features [16], [91], [104], [132], steerable pyramid transforms [7], [8], etc....
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...identification such as Neural Network [9], [96], quadratic classifier [43], [64], [75], [126], [134], rule-based classifiers [1], [90], [92], [93], Linear Discriminant Classifiers [43], [55], [83], Gaussian Mixture Model [11], [48], [50], [99], etc....
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...Fractalbased, busy-zone and topological features were used along with a Neural Network (NN) classifier for script identification....
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TL;DR: This paper demonstrates that it is important to select only the most characterizing features from handwritings and reject all those that do not contribute effectively to the process of handwriting recognition, and based mainly on fuzzy conceptual reduction by applying the Lukasiewicz implication.
Abstract: A challenging area of pattern recognition is the recognition of handwritten texts in different languages and the reduction of a volume of data to the greatest extent while preserving associations (or dependencies) between objects of the original data. Until now, only a few studies have been carried out in the area of dimensionality reduction for handedness detection from off-line handwriting textual data. Nevertheless, further investigating new techniques to reduce the large amount of processed data in this field is worthwhile. In this paper, we demonstrate that it is important to select only the most characterizing features from handwritings and reject all those that do not contribute effectively to the process of handwriting recognition. To achieve this goal, the proposed approach is based mainly on fuzzy conceptual reduction by applying the Lukasiewicz implication. Handwritten texts in both Arabic and English languages are considered in this study. To evaluate the effectiveness of our proposal approach, classification is carried out using a K-Nearest-Neighbors (K-NN) classifier using a database of 121 writers. We consider left/right handedness as parameters for the evaluation where we determine the recall/precision and F-measure of each writer. Then, we apply dimensionality reduction based on fuzzy conceptual reduction by using the Lukasiewicz implication. Our novel feature reduction method achieves a maximum reduction rate of 83.43 %, thus making the testing phase much faster. The proposed fuzzy conceptual reduction algorithm is able to reduce the feature vector dimension by 31.3 % compared to the original “best of all combined features” algorithm.
48 citations
Cites background from "Handwritten Numeral Databases of In..."
...As a result, off-line handwriting recognition continues to be an active area of research toward exploring newer techniques that would improve recognition accuracy [6]....
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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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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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