Dataset Building for Handwritten Kannada Vowels Using Unsupervised and Supervised Learning Methods

Hebbi, Chandravva; Metri, Omkar; Bhadrannavar, Manjunath; Mamatha, H. R.

doi:10.1007/978-981-16-0425-6_6

Cited by 2 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a different work Ramesh and Kumar [17] applied convolutional neural network for recognition of Kannada handwritten words, SVM classifier is employed as part of fully connected layer. Mamatha [18] proposed a dataset for handwritten Kannada vowels and classified the local binary pattern, run length count and chain codes using K-means clustering. Mahapathra et al [19] proposed a generator based methods for offline handritten character recognition using convolutional auto encoder with GAN architecture.…”

Section: Introductionmentioning

confidence: 99%

Robust recognition technique for handwritten Kannada character recognition using capsule networks

Rani

Manohar²,

Hariprasad³

et al. 2022

IJECE

View full text Add to dashboard Cite

<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%