Tools for Developing OCRs for Indian Scripts

Kumar, Manish; Kıran, Swathi; Nayani, A. S. Keerthi; Jawahar, C. V.; Narayanan, P. J.

doi:10.1109/cvprw.2003.10023

Cited by 8 publications

(3 citation statements)

References 8 publications

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“…Some combination classifiers used in Indian scripts are ANN and HMM [59], K-Means and SVM [61], MLP and SVM [62], MLP and minimum edit [63], SVM and ANN [46], fuzzy neural network [51], NN, fuzzy logic and genetic algorithm [64]. Pavan Kumar [60] used five different classifiers (two HMM and three NN based) to obtain better accuracy.…”

Section: Combinermentioning

confidence: 99%

A Review of Research on Devnagari Character Recognition

Dongre¹,

Mankar²,

Suganya³

2010

IJCA

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English Character Recognition (CR) has been extensively studied in the last half century and progressed to a level, sufficient to produce technology driven applications. But same is not the case for Indian languages which are complicated in terms of structure and computations. Rapidly growing computational power may enable the implementation of Indic CR methodologies. Digital document processing is gaining popularity for application to office and library automation, bank and postal services, publishing houses and communication technology. Devnagari being the national language of India, spoken by more than 500 million people, should be given special attention so that document retrieval and analysis of rich ancient and modern Indian literature can be effectively done. This article is intended to serve as a guide and update for the readers, working in the Devnagari Optical Character Recognition (DOCR) area. An overview of DOCR systems is presented and the available DOCR techniques are reviewed. The current status of DOCR is discussed and directions for future research are suggested.

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Section: Combinermentioning

confidence: 99%

A Review of Research on Devnagari Character Recognition

Dongre¹,

Mankar²,

Suganya³

2010

IJCA

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“…[1,2]. A survey of tools used for recognition of character for Indian languages is described in detail [3]. However, to the best of our knowledge, very few research reports are available for Odia language.…”

Section: Introductionmentioning

confidence: 99%

Odia Running Text Recognition Using Moment-Based Feature Extraction and Mean Distance Classification Technique

Nayak

2014

Advances in Intelligent Systems and Computing

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Optical character recognition (OCR) is a process of automatic recognition of character from optically scanned documents for the purpose of editing, indexing, searching, as well as reduction in storage space. Development of OCR for an Indian script is an active area of research today because the presence of a large number of letters in the alphabet set, their sophisticated combinations, and the complicated grapheme's they formed is a great challenge to an OCR designer. We are trying to develop the OCR system for Odia language, which is used as official language of Odisha (formerly known as Orissa). In this paper, we attempt to recognize the vowels, consonants, matras, and compound characters of running Odia script. At first, the given scanned text is segmented into individual Odia symbols, then, extract corresponding feature vectors, using two-dimensional moments and Hough transform (based on topological and geometrical properties), which are used to classify and recognize the symbol. We found that the proposed model can recognize up to 100 % running test having no touched characters.

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“…These standard databases include MNIST, CEDAR [2], and CENPARMI in English. Some work is also done for Indic scripts such as Bangla [3], Kannada [4], and Devnagari [5][6][7][8]. India is a multilingual and multiscript country having more than 1.2 billion population with 22 constitutional languages and 10 different scripts.…”

Section: Introductionmentioning

confidence: 99%

Development of Comprehensive Devnagari Numeral and Character Database for Offline Handwritten Character Recognition

Dongre¹,

Mankar²

2012

Applied Computational Intelligence and Soft Computing

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In handwritten character recognition, benchmark database plays an important role in evaluating the performance of various algorithms and the results obtained by various researchers. In Devnagari script, there is lack of such official benchmark. This paper focuses on the generation of offline benchmark database for Devnagari handwritten numerals and characters. The present work generated 5137 and 20305 isolated samples for numeral and character database, respectively, from 750 writers of all ages, sex, education, and profession. The offline sample images are stored in TIFF image format as it occupies less memory. Also, the data is presented in binary level so that memory requirement is further reduced. It will facilitate research on handwriting recognition of Devnagari script through free access to the researchers.

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Tools for Developing OCRs for Indian Scripts

Cited by 8 publications

References 8 publications

A Review of Research on Devnagari Character Recognition

A Review of Research on Devnagari Character Recognition

Odia Running Text Recognition Using Moment-Based Feature Extraction and Mean Distance Classification Technique

Development of Comprehensive Devnagari Numeral and Character Database for Offline Handwritten Character Recognition

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