Haar features based handwritten character recognition system for Tulu script

Antony, P J; Savitha, C K; Ujwal, U J

doi:10.1109/rteict.2016.7807784

Cited by 12 publications

(4 citation statements)

References 8 publications

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“…Haar wavelet features (Antony et al [17]) and geometrical features (Thushara et al [18]) are computed and the same set of classifiers is used. Table 3 represents the classification accuracy of Haar wavelet and geometrical features in the dataset of degraded Kannada characters.…”

Section: Experimental Analysismentioning

confidence: 99%

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Degraded character recognition from old Kannada documents

Narasimhaiah

Rangarajan

2022

IJECE

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This paper addresses preparation of a dataset of Kannada characters which are degraded and robust recognition of such characters. The proposed recognition algorithm extracts the histogram of oriented gradients (HOG) features of block sizes 4x4 and 8x8 followed by principal component analysis (PCA) feature reduction. Various classifiers are experimented with and fine K-nearest neighbor classifier performs best. The performance of proposed model is evaluated using 5-fold cross validation method and receiver operating characteristic curve. The dataset devised is of size 10440 characters having 156 classes (distinct characters). These characters are from 75 pages of not well preserved old books. A comparison of proposed model with other features like Haar wavelet and Geometrical features suggests that proposed model is superior. It is observed that the PCA reduced features followed by fine K-nearest neighbor classifier resulted in the best accuracy with acceptance rate of 98.6% and 97.9% for block sizes of 4x4 and 8x8 respectively. The experimental results show that HOG feature extraction has a high recognition rate and the system is robust even with extensively degraded characters.

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Section: Experimental Analysismentioning

confidence: 99%

“…Antony et al [17] have devised a handwritten character recognition system on Tulu script. Haar features are extracted and the classification method is AdaBoost algorithm.…”

Section: Introductionmentioning

confidence: 99%

Degraded character recognition from old Kannada documents

Narasimhaiah

Rangarajan

2022

IJECE

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“…Antony P.J et al [25] have employed Haar features in combination with Adaboost algorithm for recognizing handwritten Tulu script. Finally recognized characters are mapped into corresponding Kannada characters to make it readable by digital technology for next generation.…”

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Networks for Degraded Printed Kannada Character Recognition

T.N¹,

LalithaRangarajan²

2021

INDJCSE

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Recognition of degraded printed Kannada characters is a challenging research problem. Proposed in this paper is a deep convolutional neural network for recognition of degraded printed Kannada characters. Characters in some old Kannada texts are affected by various degradations that result in breakages and dilations of characters introducing challenges in the process of recognition. The architecture consists of three levels, the first two levels with ReLu, Max pooling layers, and the third level with just ReLu. The output of these is input to fully connected layer which performs classification of characters. Experimental analysis is carried out using 156 classes of characters each class with 100 instances. Performance is evaluated for 4 epochs with 60 iterations per epoch. Highest classification accuracy of 99.51% has been reported for 75% training.

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“…languages (Kekunnaya, 1994). Furthermore, Tulu has its own script, Tigalari, which is developed from the Grantha script, which is no longer in use(Antony et al, 2016). There are 52 letters in Tulu with 16 vowels and 36 consonants.…”

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confidence: 99%

Overview of the Shared Task on Machine Translation in Dravidian Languages

M¹,

Hegde²,

Banerjee³

et al. 2022

Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages

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This paper presents an outline of the shared task on translation of under-resourced Dravidian languages at DravidianLangTech-2022 workshop to be held jointly with ACL 2022. A description of the datasets used, approach taken for analysis of submissions and the results have been illustrated in this paper. Five sub-tasks organized as a part of the shared task include the following translation pairs: Kannada to Tamil, Kannada to Telugu, Kannada to Sanskrit, Kannada to Malayalam and Kannada to Tulu. Training, development and test datasets were provided to all participants and results were evaluated on the gold standard datasets. A total of 16 research groups participated in the shared task and a total of 12 submission runs were made for evaluation. Bilingual Evaluation Understudy (BLEU) score was used for evaluation of the translations.

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Haar features based handwritten character recognition system for Tulu script

Cited by 12 publications

References 8 publications

Degraded character recognition from old Kannada documents

Degraded character recognition from old Kannada documents

Deep Convolutional Neural Networks for Degraded Printed Kannada Character Recognition

Overview of the Shared Task on Machine Translation in Dravidian Languages

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