Computer recognition of printed Tamil characters

Siromoney, Gift; Chandrasekaran, R. M.; Chandrasekaran, M.

doi:10.1016/0031-3203(78)90032-8

Cited by 52 publications

(15 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Siromony et al [111] described a method for recognition of machine printed Tamil characters using an encoded character string dictionary. The scheme employs string features extracted by row-and column-wise scanning of character matrix.…”

Section: Studies On Tamil Character Recognitionmentioning

confidence: 99%

Indian script character recognition: a survey

Pal

Chaudhuri

2004

Pattern Recognition

456

178

View full text Add to dashboard Cite

Section: Studies On Tamil Character Recognitionmentioning

confidence: 99%

Indian script character recognition: a survey

Pal

Chaudhuri

2004

Pattern Recognition

456

178

View full text Add to dashboard Cite

“…Siromoney et al [8] illustrated a scheme for recognition of machine printed Tamil characters using an encoded character string dictionary.Chinnuswamy et al [9] put forwarded an idea for hand-printed Tamil character recognition. In this, the characters are consisting of line-like elements, called primitives.Topological matching procedure to calculate the correlation coefficients and then maximizes the correlation coefficient.Suresh et al [10] elucidates the fuzzy concept on handwritten Tamil characters using a feature called distance from the frame and a membership function.…”

Section: Previous Ocr Research In Tamilmentioning

confidence: 99%

Recognition of Tamil Handwritten Characters using Daubechies Wavelet Transforms and Feed-Forward Backpropagation Network

MJose¹,

Wahi²

2013

IJCA

View full text Add to dashboard Cite

This article suggests wavelet transform based feature extraction technique for extracting robust features from Tamil handwritten characters. The algorithm uses feed-forward back propagation neural network as the classifier. It presents the relevant features of Tamil script and describes various techniques used for character recognition. In common, all the pattern recognition tasks focus on extracting more differentiating features. This is the most important and complicated job. The proposed system concentrates on two dimensional discrete wavelet transformations for extraction of features. For multiresolution analysis of images, Wavelet Transform is used. This capability can be used to study the character image in different frequency bands. Localized basis functions of WT are used for extracting localized features of a character image. This enables us to obtain more distinct traits as features for each character. Feed forward back propagation neural network is one of the general neural network architectures and this architecture can be applied to many different tasks and is very popular.

show abstract

“…Because Recognition of any handwritten characters with respect to any language is difficult, since, the handwritten characters differ not only from person to person but also according to the state of mood of the same person. Among different branches of character recognition it is easier to recognize English alphabets [20] and Numerals [21,22] than Tamil characters [23,24] . The process of handwriting recognition involves extraction of some defined characteristics called features to classify an unknown handwritten character into one of the known classes.…”

Section: Handwritten Character Recognitionmentioning

confidence: 99%

An Improved Handwritten Tamil Character Recognition System using Octal Graph

Kannan¹,

Prabhakar²

2008

J. of Computer Science

View full text Add to dashboard Cite

Problem Statement: Handwriting recognition has attracted voluminous research in recent times. The segmentation and recognition of the characters from handwritten scripts incorporates considerable overhead. Almost all the existing handwritten character recognition techniques use neural network approach, which requires lot of preprocessing and hence accomplishing these problems using neural network is a tedious task. Approach: In this study we propose a novel solution for performing character recognition in Tamil, the official language of the south Indian province of Tamil Nadu. Pursued by the preprocessing techniques, Segmentation, Normalization and Feature Extraction the approach utilizes octal graph conversion for recognizing off-line handwritten Tamil characters which improves the slant correction. The graph tries to represent the basic form of a letter independent of the style of writing. Using the weights of the graphs and by the appropriate feature matching with the predefined characters, the written characters are recognized. Results: The performance evaluation of off line handwritten Tamil character using octal graph conversion and the metrics based on ranks of the letters proves good Recognition Efficiency Conclusion: We show that, in practise, the proposed approach produces near optimal results besides outperforming the other methodologies in existence. Results indicate that the approach can be used for character recognition in other Indic scripts as well.

show abstract

Computer recognition of printed Tamil characters

Cited by 52 publications

References 3 publications

Indian script character recognition: a survey

Indian script character recognition: a survey

Recognition of Tamil Handwritten Characters using Daubechies Wavelet Transforms and Feed-Forward Backpropagation Network

An Improved Handwritten Tamil Character Recognition System using Octal Graph

Contact Info

Product

Resources

About