A Review of Research on Devnagari Character Recognition

Dongre, Vikas J.; Mankar, Vijay H.; Suganya, G.

doi:10.5120/1653-2224

Cited by 38 publications

(9 citation statements)

References 42 publications

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“…For any handwritten document where text orientation varies heavily for different users by using cognition reading strategy in wrong orientation should be recognized properly by automatically correcting the orientation [6]. Based on such a technique, higher accuracy ratio of about 95% is achieved [7] in experimental evaluation.…”

Section: Literature Reviewmentioning

confidence: 99%

Cognitive Reading and Character Recognition in Image Processing Techniques

Kasthuri

Sivaprasatham

2020

SN COMPUT. SCI.

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The trend of researches in cognitive reading has become so popular in programming area in the field of computer science from late 1990s when scientists and researchers show more interests in computational approaches are complex in nature to derive from a known algorithm of solution. For instance, in the research areas of biology, medicine and human management sciences there are various problems where we need cognitive reading to deliver a complex and in-exact solution when there is no polynomial time to arrive at an exact solution. This article explains some of the methods in cognitive reading in image processing for character recognition and briefly discusses the steps involved in the process of character recognition in image processing.

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Section: Literature Reviewmentioning

confidence: 99%

Cognitive Reading and Character Recognition in Image Processing Techniques

Kasthuri

Sivaprasatham

2020

SN COMPUT. SCI.

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“…Thus neural networks which are pretty much heuristic in nature are greatly appropriate for this type of issue [19]. A neural network is an ascertaining architecture that includes enormously parallel interconnection of flexible node processors.…”

Section: Neural Networkmentioning

confidence: 99%

“…The main statistical methods that are performed in the area of OCR [19] are Nearest Neighbor (NN), Likelihood or Bayes classifier, Clustering Analysis, Hidden Markov Modelling (HMM), Fuzzy Set Reasoning, and Quadratic classifier.…”

Section: Statistical Techniquesmentioning

confidence: 99%

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A Detailed Analysis of Optical Character Recognition Technology

Hamad

Kaya

2016

International Journal of Applied Mathematics, Electronics and Computers

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Abstract:In many different fields, there is a high demand for storing information to a computer storage disk from the data available in printed or handwritten documents or images to later re-utilize this information by means of computers. One simple way to store information to a computer system from these printed documents could be first to scan the documents and then store them as image files. But to re-utilize this information, it would very difficult to read or query text or other information from these image files. Therefore a technique to automatically retrieve and store information, in particular text, from image files is needed. Optical character recognition is an active research area that attempts to develop a computer system with the ability to extract and process text from images automatically. The objective of OCR is to achieve modification or conversion of any form of text or text-containing documents such as handwritten text, printed or scanned text images, into an editable digital format for deeper and further processing. Therefore, OCR enables a machine to automatically recognize text in such documents. Some major challenges need to be recognized and handled in order to achieve a successful automation. The font characteristics of the characters in paper documents and quality of images are only some of the recent challenges. Due to these challenges, characters sometimes may not be recognized correctly by computer system. In this paper we investigate OCR in four different ways. First we give a detailed overview of the challenges that might emerge in OCR stages. Second, we review the general phases of an OCR system such as pre-processing, segmentation, normalization, feature extraction, classification and post-processing. Then, we highlight developments and main applications and uses of OCR and finally, a brief OCR history are discussed. Therefore, this discussion provides a very comprehensive review of the state-of-the-art of the field.

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“…[1,2]. A survey of tools used for recognition of character for Indian languages is described in detail [3].…”

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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A Review of Research on Devnagari Character Recognition

Cited by 38 publications

References 42 publications

Cognitive Reading and Character Recognition in Image Processing Techniques

Cognitive Reading and Character Recognition in Image Processing Techniques

A Detailed Analysis of Optical Character Recognition Technology

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

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