Online Handwritten Kannada Word Recognizer with Unrestricted Vocabulary

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

doi:10.1109/icfhr.2010.100

Cited by 8 publications

(4 citation statements)

References 7 publications

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“…Rituraj Kumar et al [11] contributed work on Online handwritten Kannada characters. The statistical Dynamic Time Wrapping is used as a classifier that uses X, Y Coordinates and their first derivative as features [11].…”

Section: Related Workmentioning

confidence: 99%

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Analysis of Telugu Palm Leaf Character Recognition Using 3D Feature

Lakshmi¹,

Sastry

Ramakrishnan

et al. 2015

2015 International Conference on Computational Intelligence and Networks

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This paper deals with the recognition of Telugu characters on palm leaf using statistical features. Handwritten character recognition has various applications in post offices, reading aids for blind, library automation and multimedia design. Palm leaf manuscripts contain religious texts and a host of subjects such as art, medicine, music, astrology, law and astronomy. There is an inherent 3D feature for characters on palm leaf called depth. This depth is proportional to the writers stylus pressure applied at each pixel point. This 3D feature of every pixel in an image is used to recognize the palm leaf characters in the present work. The image is divided into zones and the sum of the pixel intensities in each zone is used as a feature vector to recognize the palm leaf characters. As per the literature survey, the recognition accuracy for handwritten characters is less than 60% and also very less amount of work is done for palm leaf character recognition. Using the proposed method the best recognition accuracy obtained for palm leaf characters is 96%.

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Section: Related Workmentioning

confidence: 99%

“…The statistical Dynamic Time Wrapping is used as a classifier that uses X, Y Coordinates and their first derivative as features [11]. The work is carried out for 295 classes of Kannada characters, numerals, punctuations and special symbols.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Telugu Palm Leaf Character Recognition Using 3D Feature

Lakshmi¹,

Sastry

Ramakrishnan

et al. 2015

2015 International Conference on Computational Intelligence and Networks

View full text Add to dashboard Cite

show abstract

“…46 times faster classification rate is achieved Using SDTW over DTW with comparable accuracy of 88%. Kunwar et al [44] also developed a system to perform recognition of entire word. Writer independent word level recognition of 80% is reported using SDTW as classifier with estimate 2 of first 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%