A Framework for Degraded Kannada Character Recognition

Sandhya, N.; Krishnan, Rahul; Babu, D. R. Ramesh

doi:10.1007/978-3-030-51859-2_67

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…An accuracy of 93.2% is obtained towards classification of basic vowels and consonants. Further Sandhya et al [13] investigated a framework for degraded Kannada character recognition by addressing character stroke breakages, complex compound characters in printed documents using machine learning based techniques. In an another work Ucar et al [14] used capsule network for classification of Kannada handwritten digit datasets.…”

Section: Introductionmentioning

confidence: 99%

Robust recognition technique for handwritten Kannada character recognition using capsule networks

Rani

Manohar²,

Hariprasad³

et al. 2022

IJECE

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<p>Automated reading of handwritten Kannada documents is highly challenging due to the presence of vowels, consonants and its modifiers. The variable nature of handwriting styles aggravates the complexity of machine based reading of handwritten vowels and consonants. In this paper, our investigation is inclined towards design of a deep convolution network with capsule and routing layers to efficiently recognize Kannada handwritten characters. Capsule network architecture is built of an input layer, two convolution layers, primary capsule, routing capsule layers followed by tri-level dense convolution layer and an output layer. For experimentation, datasets are collected from more than 100 users for creation of training data samples of about 7769 comprising of 49 classes. Test samples of all the 49 classes are again collected separately from 3 to 5 users creating a total of 245 samples for novel patterns. It is inferred from performance evaluation; a loss of 0.66% is obtained in the classification process and for 43 classes precision of 100% is achieved with an accuracy of 99%. An average accuracy of 95% is achieved for all remaining 6 classes with an average precision of 89%.</p>

show abstract

Section: Introductionmentioning

confidence: 99%

Robust recognition technique for handwritten Kannada character recognition using capsule networks

Rani

Manohar²,

Hariprasad³

et al. 2022

IJECE

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show abstract

“…A stepwise framework proposed by Sandhya .Net al [7] for recognition of degraded Kannada characters as follows: Pre-processing, rebuilding of character components, followed by feature extraction and classification. An accuracy of 99% has been observed.…”

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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A Framework for Degraded Kannada Character Recognition

Cited by 2 publications

References 10 publications

Robust recognition technique for handwritten Kannada character recognition using capsule networks

Robust recognition technique for handwritten Kannada character recognition using capsule networks

Deep Convolutional Neural Networks for Degraded Printed Kannada Character Recognition

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