Journal ArticleDOI
Offline Recognition of Devanagari Script: A Survey
R. Jayadevan,Satish R. Kolhe,Pradeep M. Patil,Umapada Pal +3 more
- Vol. 41, Iss: 6, pp 782-796
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TLDR
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.read more
Citations
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Journal ArticleDOI
Handwriting Recognition in Indian Regional Scripts: A Survey of Offline Techniques
TL;DR: Various feature extraction and classification techniques associated with the offline handwriting recognition of the regional scripts are discussed in this survey, which will serve as a compendium not only for researchers in India, but also for policymakers and practitioners in India.
Journal ArticleDOI
HMM-based Indic handwritten word recognition using zone segmentation
TL;DR: An efficient word recognition framework by segmenting the handwritten word images horizontally into three zones (upper, middle and lower) and then recognize the corresponding zones to reduce the number of distinct component classes compared to the total number of classes in Indic scripts is proposed.
Journal ArticleDOI
A survey on optical character recognition for Bangla and Devanagari scripts
Soumen Bag,Gaurav Harit +1 more
TL;DR: A review of OCR work on Indian scripts, mainly on Bangla and Devanagari—the two most popular scripts in India, and the various methodologies and their reported results are presented.
Journal ArticleDOI
Multilingual Character Segmentation and Recognition Schemes for Indian Document Images
Parul Sahare,Sanjay B. Dhok +1 more
TL;DR: In this paper, robust algorithms for character segmentation and recognition are presented for multilingual Indian document images of Latin and Devanagari scripts, where primary segmentation paths are obtained using structural property of characters, whereas overlapped and joined characters are separated using graph distance theory.
Proceedings ArticleDOI
Handwritten Hindi character recognition using k-means clustering and SVM
Akanksha Gaur,Sunita Yadav +1 more
TL;DR: Recognition of Hindi characters is done by using a three step procedure, in which binarization of the image and separations of characters are performed.
References
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Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Journal ArticleDOI
Indian script character recognition: a survey
Umapada Pal,Bidyut B. Chaudhuri +1 more
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.
Journal ArticleDOI
An overview of character recognition focused on off-line handwriting
TL;DR: The historical evolution of CR systems is presented, the available CR techniques, with their superiorities and weaknesses, are reviewed and directions for future research are suggested.
Journal ArticleDOI
Character recognition—a review
V. K. Govindan,A P Shivaprasad +1 more
TL;DR: There still is a great gap between human reading and machine reading capabilities, and a great amount of further effort is required to narrow-down this gap, if not bridge it.