Computer Recognition of Tamil, Malayalam and Devanagari Characters

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

doi:10.1080/03772063.1984.11453261

Cited by 12 publications

(7 citation statements)

References 5 publications

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“…Various feature extraction approaches have been proposed for character recognition system (Rahman, et al, 2002). The problems faced in handwritten numeral recognition has been studied while using the techniques like Dynamic programming, HMM, neural network, Knowledge system and combinations of above techniques (Chandrasekaran, et al,1984). Wider ranging work has been carried out for digit recognition in so many languages like English, Chinese, Japanese, and Arabic.…”

Section: Dataset Descriptionmentioning

confidence: 99%

Handwritten Digit Recognition Using Machine Learning Algorithms

Shamim

Miah

Sarker

et al. 2018

Indonesian J. Sci. Technol

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Handwritten character recognition is one of the practically important issues in pattern recognition applications. The applications of digit recognition include in postal mail sorting, bank check processing, form data entry, etc. The main problem lies within the ability on developing an efficient algorithm that can recognize hand written digits, which is submitted by users by the way of a scanner, tablet, and other digital devices. This paper presents an approach to off-line handwritten digit recognition based on different machine learning techniques. The main objective of this paper is to ensure the effectiveness and reliability of the approached recognition of handwritten digits. Several machines learning algorithms (i.e. Multilayer Perceptron, Support Vector Machine, Naïve Bayes, Bayes Net, Random Forest, J48, and Random Tree) have been used for the recognition of digits using WEKA. The experimental results showed that the highest accuracy was obtained by Multilayer Perceptron with the value of 90.37%.

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Section: Dataset Descriptionmentioning

confidence: 99%

Handwritten Digit Recognition Using Machine Learning Algorithms

Shamim

Miah

Sarker

et al. 2018

Indonesian J. Sci. Technol

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“…Recognition of character/numeral in foreign languages like English, Chinese, Japanese, and Arabic are reported by many authors. In the Indian context, some major works are reported in Devanagari, Tamil, Bengali and Kannada numeral recognition [4,5,6]. Dinesh Acharya et.…”

Section: Introductionmentioning

confidence: 99%

"Kannada, Telugu and Devanagari Handwritten Numeral Recognition with Probabilistic Neural Network: A Script Independent Approach"

Dhandra¹,

Benne²,

Hangarge³

2011

IJCA

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“…Extensive work has been carried out for recognition of characters and numerals in foreign languages like English, Chinese, Japanese, and Arabic. With respect to the Indian scripts, a major work can be found in [3,4] on Bengali and Tamil scripts, where as the work on handwritten Kannada and Telugu numerals recognition is in infant stage. Recognition of handwritten Kannada and Telugu characters are complex task due to the unconstrained shapes, variation in writing style and different kinds of noise that break the strokes primitives in the characters or change in their topology.…”

Section: Introductionmentioning

confidence: 99%

A Script Independent Approach for Handwritten Bilingual Kannada and Telugu Digits Recognition

Bv¹,

Mukarambi²,

Hangarge³

2011

Int J Mach Intell

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Abstract-In this paper, handwritten Kannada and Telugu digits recognition system is proposed based on zone features. The digit image is divided into 64 zones. For each zone, pixel density is computed. The KNN and SVM classifiers are employed to classify the Kannada and Telugu handwritten digits independently and achieved average recognition accuracy of 95.50%, 96.22% and 99.83%, 99.80% respectively. For bilingual digit recognition the KNN and SVM classifiers are used and achieved average recognition accuracy of 96.18%, 97.81% respectively.

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Computer Recognition of Tamil, Malayalam and Devanagari Characters

Cited by 12 publications

References 5 publications

Handwritten Digit Recognition Using Machine Learning Algorithms

Handwritten Digit Recognition Using Machine Learning Algorithms

"Kannada, Telugu and Devanagari Handwritten Numeral Recognition with Probabilistic Neural Network: A Script Independent Approach"

A Script Independent Approach for Handwritten Bilingual Kannada and Telugu Digits Recognition

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