An intelligent character recognizer for Telugu scripts using multiresolution analysis and associative memory

Pujari, Arun K.; Naidu, C. D.; Rao, M. Sreenivasa; Jinaga, B. C.

doi:10.1016/j.imavis.2004.03.027

Cited by 38 publications

(13 citation statements)

References 29 publications

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“…For several languages, specifically indic languages where the number of symbols are in hundreds, automatic recognition of printed/handwritten characters poses a unique challenge for multiclass classification. Though there have been substantial research in recognition of Indic Characters [1], [2], [3], there has not been much of attentions paid to Odia characters [4], [5]. Akshara is considered to be the atomic symbol in Indic scripts and an Akshara can be an independent vowel, a consonant, a consonant with vowel modifier or a combination of multiple consonants and vowels.…”

Section: Introductionmentioning

confidence: 99%

Greedy partitioning based tree structured multiclass SVM for Odia OCR

Sahu

Pujari

Kumar

et al. 2015

2015 Fifth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)

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There have been many proposals to extend the basic two-class SVM classifier for multiclass classification and it is established that among these extensions binary-structured hierarchical SVMs is the most efficient computationally. However, determining an effective binary structure by recursively dividing the classes is a major research issue. We describe a new classifier, GP-SVM, based on greedy partitioning of classes and demonstrate that GP-SVM gives better classification accuracy than all major combinational techniques besides having the computational advantages. The advantages of GP-SVM is better realized when the number of classes is large. We demonstrate this advantage in recognition of printed Odia character. We built a corpus of 10025 tagged Odia aksharas collected over multiple printed documents of different fonts. We used a very modest number of features. GP-SVM with 133 classes yielded 95% accuracy of recognition. During the learning process of GP-SVM, the proposed system could learn the taxonomy of character-shapes of Odia script.

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Section: Introductionmentioning

confidence: 99%

Greedy partitioning based tree structured multiclass SVM for Odia OCR

Sahu

Pujari

Kumar

et al. 2015

2015 Fifth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)

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“…In [10], character recognition for the Hindi language using an artificial neural network where they considered the histogram based on projection and pixel values in their features part has been proposed. A commercially viable Telugu character recognizer has been reported in [11]. Wavelet multi-resolution analysis and associative memory models have been used for feature extraction and recognition purposes, respectively.…”

Section: Introductionmentioning

confidence: 99%

A Contour Descriptors-Based Generalized Scheme for Handwritten Odia Numerals Recognition

2014

J Inf Process Syst

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In this paper, we propose a novel feature for recognizing handwritten Odia numerals. By using polygonal approximation, each numeral is segmented into segments of equal pixel counts where the centroid of the character is kept as the origin. Three primitive contour features namely, distance (l), angle (θ), and arc-tochord ratio (r), are extracted from these segments. These features are used in a neural classifier so that the numerals are recognized. Other existing features are also considered for being recognized in the neural classifier, in order to perform a comparative analysis. We carried out a simulation on a large data set and conducted a comparative analysis with other features with respect to recognition accuracy and time requirements. Furthermore, we also applied the feature to the numeral recognition of two other languagesBangla and English. In general, we observed that our proposed contour features outperform other schemes.

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“…The encoding and string representation of the character image which we describe in later section. There are many approaches in previous work [5][6] for character recognition, with different strategies such as using fringe distance, template matching [7] and wavelet analysis [8]. Now we build up some essential background for our approach.…”

Section: Introductionmentioning

confidence: 99%

A Character Recognition Approach using Freeman Chain Code and Approximate String Matching

KumarPradhan¹,

Sarker²,

Das³

2013

IJCA

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This paper deals with a syntactic approach for character recognition using approximate string matching and chain coding of characters. Here we deal only with the classification of characters and not on other phase of the character recognition process in a Optical character Recognition. The character image is first normalized to a specified size then by boundary detection process we detect the boundary of the character image. The character now converted to boundary curve representation of the characters. Then the curve is encoded to a sequence of numbers using Freeman chain coding. The coding scheme gives a sequence of numbers ranges from 0 to 7. Now the characters are in form of strings. For training set we will get a set of strings which is stored in the trie. The extracted unclassified character is also converted to string and searched in the trie. As we are dealing with the character which can be of different orientation so the searching is done with approximate string matching to support noisy character that of different orientation. For approximate string matching we use Look Ahead Branch and Bound scheme to prune path and make the approximation accurate and efficient. As we are using trie data structure, so it take uniform time and don't dependent on the size of the input. When we performed our experimentation for noiseless character that is printed character it successfully recognize all characters. But when we tested with the different variation of the character then it detect most of the character except some noisy character.

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An intelligent character recognizer for Telugu scripts using multiresolution analysis and associative memory

Cited by 38 publications

References 29 publications

Greedy partitioning based tree structured multiclass SVM for Odia OCR

Greedy partitioning based tree structured multiclass SVM for Odia OCR

A Contour Descriptors-Based Generalized Scheme for Handwritten Odia Numerals Recognition

A Character Recognition Approach using Freeman Chain Code and Approximate String Matching

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