Unconstrained Handwritten Malayalam Character Recognition using Wavelet Transform and Support vector Machine Classifier

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

doi:10.1016/j.proeng.2012.01.904

Cited by 44 publications

(19 citation statements)

References 8 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: 62%

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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: 62%

“…Bhowmik et al [10] provides SVM based hierarchical classification schemes for recognition of handwritten Bangla characters. To the best of our knowledge, the use of SVM in offline handwritten Malayalam characters was reported only in our previous paper [11].…”

Section: Classificationmentioning

confidence: 99%

A System for Offline Recognition of Handwritten Characters in Malayalam Script

John¹,

Balakrishnan²,

Pramod³

2013

IJIGSP

Self Cite

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“…The SVM is a popular supervised learning algorithm that has been employed in many real-world problems such as fault diagnosis [58], image classification [59], bioinformatics [60], geographical analysis [61] and hand-written character recognition [62]. Originally, the SVM is designed to solve binary classification problems, but multi-class extensions are also available [63].…”

Section: Svm In Qa and Data Classificationmentioning

confidence: 99%

Review of data mining applications for quality assessment in manufacturing industry: support vector machines

Rostami

Dantan

Homri

2015

Int. J. Metrol. Qual. Eng.

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is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. AbstractIn many modern manufacturing industries, data that characterize the manufacturing process are electronically collected and stored in the databases.Due to advances in data collection systems and analysis tools, data mining (DM)has widely been applied for quality assessment (QA) in manufacturing industries.In DM, the choice of technique to use in analyzing a dataset and assessing the quality depend on the understanding of the analyst. On the other hand, with the advent of improved and efficient prediction techniques, there is a need for an analyst to know which tool performs best for a particular type of data set.Although a few review papers have recently been published to discuss DM applications in manufacturing for QA, this paper provides an extensive review to investigate the application of a special DM technique, namely support vector machine (SVM) to solve QA problems. The review provides a comprehensive analysis of the literature from various points of view as DM preliminaries, data preprocessing, DM applications for each quality task, SVM preliminaries, and application results. Summary tables and figures are also provided besides to the analyses. Finally, conclusions and future research directions are provided.

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“…[8] have done segmentation using projection analysis and connected component labeling. In that they have used horizontal projection profile for the line segmentation and isolated character using connected component labeling algorithm.…”

Section: Segmentationmentioning

confidence: 99%

“…[8] used SVM classifier with RBF (Radial Basis Function) kernel for the classification of Malayalam characters. The feature space is linearly inseparable so using RBF kernel it is mapped into a high dimensional space and becomes linearly separable.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

A Survey on Handwritten Character Recognition Techniques for Various Indian Languages

Dholakia¹

2015

IJCA

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Handwritten character recognition is always an interesting area of pattern recognition for research in the field of image processing. Many researchers have presented their work in this area and still research is undergoing to achieve high accuracy. This paper is mainly concerned for the people who are working on the character recognition and review of work to recognize handwritten character for various Indian languages. The objective of this paper is to describe the set of preprocessing, segmentation, feature extraction and classification techniques.

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Unconstrained Handwritten Malayalam Character Recognition using Wavelet Transform and Support vector Machine Classifier

Cited by 44 publications

References 8 publications

A System for Offline Recognition of Handwritten Characters in Malayalam Script

A System for Offline Recognition of Handwritten Characters in Malayalam Script

Review of data mining applications for quality assessment in manufacturing industry: support vector machines

A Survey on Handwritten Character Recognition Techniques for Various Indian Languages

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