About: Devanagari is a(n) research topic. Over the lifetime, 655 publication(s) have been published within this topic receiving 7428 citation(s). The topic is also known as: Deva nagari & Hindi Script.
Papers published on a yearly basis
01 Sep 2004-Pattern Recognition
TL;DR: A review of the OCR work done on Indian language scripts and the scope of future work and further steps needed for Indian script OCR development is presented.
Abstract: Intensive research has been done on optical character recognition (OCR) and a large number of articles have been published on this topic during the last few decades. Many commercial OCR systems are now available in the market. But most of these systems work for Roman, Chinese, Japanese and Arabic characters. There are no sufficient number of work on Indian language character recognition although there are 12 major scripts in India. In this paper, we present a review of the OCR work done on Indian language scripts. The review is organized into 5 sections. Sections 1 and 2 cover introduction and properties on Indian scripts. In Section 3, we discuss different methodologies in OCR development as well as research work done on Indian scripts recognition. In Section 4, we discuss the scope of future work and further steps needed for Indian script OCR development. In Section 5 we conclude the paper.
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.
01 Jul 1977-Pattern Recognition
TL;DR: A method is presented for the machine recognition of constrained, hand printed Devanagari characters, where each stage of decision making narrows down the choice regarding the class membership of the input token.
Abstract: A method is presented for the machine recognition of constrained, hand printed Devanagari characters. A set of very simple primitives is used, and all the Devanagari characters are looked upon as a concatenation of these primitives. Most of the decisions are taken on the basis of the presence/absence or positional relationship of these primitives; and the decision process is a multistage process, where each stage of decision making narrows down the choice regarding the class membership of the input token.
01 Apr 2002-Pattern Recognition
TL;DR: A two pass algorithm for the segmentation and decomposition of Devanagari composite characters/symbols into their constituent symbols and a recognition rate has been achieved on the segmented conjuncts.
Abstract: Devanagari script is a two dimensional composition of symbols It is highly cumbersome to treat each composite character as a separate atomic symbol because such combinations are very large in number This paper presents a two pass algorithm for the segmentation and decomposition of Devanagari composite characters/symbols into their constituent symbols The proposed algorithm extensively uses structural properties of the script In the first pass, words are segmented into easily separable characters/composite characters Statistical information about the height and width of each separated box is used to hypothesize whether a character box is composite In the second pass, the hypothesized composite characters are further segmented A recognition rate of 85 percent has been achieved on the segmented conjuncts The algorithm is designed to segment a pair of touching characters
••01 Nov 2011
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.
Abstract: In India, more than 300 million people use Devanagari script for documentation. There has been a significant improvement in the research related to the recognition of printed as well as handwritten Devanagari text in the past few years. State of the art from 1970s of machine printed and handwritten Devanagari optical character recognition (OCR) is discussed in this paper. All feature-extraction techniques as well as training, classification and matching techniques useful for the recognition are discussed in various sections of the paper. An attempt is made to address the most important results reported so far and it is also tried to highlight the beneficial directions of the research till date. Moreover, the paper also contains a comprehensive bibliography of many selected papers appeared in reputed journals and conference proceedings as an aid for the researchers working in the field of Devanagari OCR.
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