Unrestricted Kannada online handwritten akshara recognition using SDTW

Kunwar, Rituraj; Mohan, Prajval; Shashikiran, K; Ramakrishnan, A. G.

doi:10.1109/spcom.2010.5560515

Cited by 8 publications

(4 citation statements)

References 8 publications

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“…System for unrestricted Kannada online HCR is proposed by Kunwar et al [43]. Statistical dynamic time warping (SDTW) has been employed to classify Kannada characters with x-y coordinates of the trace and their first order derivatives as features.…”

Section: Recognition Of Kannada Scriptmentioning

confidence: 99%

“…Various Designers have been actively involved in developing online Handwritten character recognition systems for Indian scripts (N Joshi et al [6,27]; A Sharma et al [11,51], R.K Sharma et al [11,14,15,16], Sachan and Lehal et al [12,13] ,U. Bhattacharya et al [17,19,20], A G. Ramkrishnan etal. [6,27,30,31,32,33,34,43,44,46], R. Kunwar et al [30,31,44]). Little work has also been reported for bilingual Online HCR(S Lakshami et al [7,35], A.Arora and Namboodiri et al [38]) and HCR for Mobile Devices (A Sharma et al [52]).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Online Handwritten Text Recognition for Indian Scripts

Kaur¹,

Sharma²

2017

IOSR JVSP

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Section: Recognition Of Kannada Scriptmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Online Handwritten Text Recognition for Indian Scripts

Kaur¹,

Sharma²

2017

IOSR JVSP

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“…The relation with the standard deviation, σ, of a Gaussian probability density function is given by (2).…”

Section: B Smoothingmentioning

confidence: 99%

“…Kunte et al [1] in the year 2000 using wavelet features and neural network as classifier, with a recognition accuracy of 95%. Later in 2010 Rituraj Kunwar et al [2] used statistical dynamic space warping (SDSW) classifier reporting accuracies of 88% at the Akshara level and 80% at the word level. Mahadeva Prasad et al [3] …”

Section: Introductionmentioning

confidence: 99%

Comparison of smoothing techniques and recognition methods for online Kannada character recognition system

Shwetha

Ramya

2014

2014 International Conference on Advances in Engineering &Amp; Technology Research (ICAETR - 2014)

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This paper aimed at working on Online Recognition of Handwritten Kannada Characters. The recognition was done for the Top, Middle and Bottom strokes of Kannada characters. Genius MousePen i608X was used to collect the handwritten character samples to build the database. Handwritten character samples were collected for each character from a particular target-group which includes people who are native to Kannada language and belong to different age groups. These samples were semi-automatically validated, pre-processed and features were extracted. Segmentation of characters was done to divide the strokes into top stroke, middle stroke and bottom stroke. These segmented strokes were individually processed. The pre-processing techniques used in the project include removal of duplicated points, smoothing, interpolating missing points, resampling of points and size normalization. Smoothing techniques was compared for Gaussian and Moving Average Smoothing. Dominant point, writing direction and the curvature features were also extracted. In addition to this, recognition was carried out by KNN and SVM pattern recognition methods and a second level of verification rules was incorporated, yielding a maximum recognition rate of 92.5% for KNN and 94.35% for SVM.

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