Offline handwritten Malayalam Character Recognition based on chain code histogram

John, J.; Pramod, K Varshney; Balakrishnan, Kalpana

doi:10.1109/icetect.2011.5760215

Cited by 28 publications

(14 citation statements)

References 18 publications

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“…Here the dimension is reduced from 800 to 200. These results show significant improvement to all our previous works [11][12][13]. …”

Section: Data Set Isupporting

confidence: 70%

A System for Offline Recognition of Handwritten Characters in Malayalam Script

John¹,

Balakrishnan²,

Pramod³

2013

IJIGSP

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Abstract-In this paper, we propose a handwritten character recognition system for Malayalam language. The feature extraction phase consists of gradient and curvature calculation and dimensionality reduction using Principal Component Analysis. Directional information from the arc tangent of gradient is used as gradient feature. Strength of gradient in curvature direction is used as the curvature feature. The proposed system uses a combination of gradient and curvature feature in reduced dimension as the feature vector. For classification, discriminative power of Support Vector Machine (SVM) is evaluated. The results reveal that SVM with Radial Basis Function (RBF) kernel yield the best performance with 96.28% and 97.96% of accuracy in two different datasets. This is the highest accuracy ever reported on these datasets.

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“…Here the dimension is reduced from 800 to 200. These results show significant improvement to all our previous works [11][12][13]. …”

Section: Data Set Isupporting

confidence: 70%

A System for Offline Recognition of Handwritten Characters in Malayalam Script

John¹,

Balakrishnan²,

Pramod³

2013

IJIGSP

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“…Jagadeesh Kannan et al [9] used octal graph method for the recognition of the Tamil handwritten characters. Here, the character return on the octal graph's pixel is converted into the node of the graph.…”

Section: Literature Surveymentioning

confidence: 99%

“…A few models that have been applied for the HCR system include motor models [16], structure-based models [17,18], stochastic models [8], and learning-based [9]. Learning-based models have received wide attention for pattern recognition problems [19,20,21] (Figs.…”

Section: Neural Classifiermentioning

confidence: 99%

Handwritten Tamil Character Recognition Using Zernike Moments and Legendre Polynomial

Wahi¹,

Sundaramurthy²,

Poovizhi³

2014

Advances in Intelligent Systems and Computing

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Optical character recognition systems have been effectively developed for recognizing the printed characters of many non-Indian languages such as English and Chinese. At early stages, few research works were carried out for recognizing the handwritten characters, and now, various efforts are on the way for the development of efficient systems for recognizing the Indian languages, especially for Tamil, a south Indian language widely used in Tamilnadu, Pudhucherry, Singapore, and Srilanka. In this paper, an OCR system is developed for the recognition of basic characters in handwritten Tamil language, which can handle different font sizes and font types. Zernike moments and Legendre polynomial which have been used in pattern recognition are used in this system to extract the features of handwritten Tamil characters. A comparative study is performed in this paper. Feed forward back propagation neural classifier has been used for the classification of Tamil characters.

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“…The performance of wavelet transform of projection profiles using 12 different wavelet filters were analyzed in [4]. In [5], recognition of Malayalam vowels was done using chain code histogram and image centroid. They have also proposed another method for Malayalam character recognition using Haar wavelet transform and SVM classifier [6].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Different Feature Extraction Techniques for Offline Malayalam Character Recognition

Chacko¹,

Dhanya²

2014

Computational Intelligence in Data Mining - Volume 2

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Offline Handwritten Character Recognition of Malayalam scripts have gained remarkable attention in the past few years. The complicated writing style of Malayalam characters with loops and curves make the recognition process highly challenging. This paper presents a comparative study of Malayalam character recognition using 4 different feature sets-Zonal features, Projection histograms, Chain code histograms and Histogram of Oriented Gradients. The performance of these features for isolated Malayalam vowels and 5 consonants are evaluated in this study using feedforward neural networks as classifier. The final recognition results were computed using a 5 fold cross validation scheme. The best recognition accuracy of 94.23 % was obtained in this study using Histogram of Oriented Gradients features.

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Offline handwritten Malayalam Character Recognition based on chain code histogram

Cited by 28 publications

References 18 publications

A System for Offline Recognition of Handwritten Characters in Malayalam Script

A System for Offline Recognition of Handwritten Characters in Malayalam Script

Handwritten Tamil Character Recognition Using Zernike Moments and Legendre Polynomial

A Comparative Study of Different Feature Extraction Techniques for Offline Malayalam Character Recognition

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