Gabor Wavelets Based Word Retrieval from Kannada Documents

Hangarge, Mallikarjun; Veershetty, C.; Pardeshi, Rajmohan; Dhandra, B.V.

doi:10.1016/j.procs.2016.03.057

Cited by 10 publications

(5 citation statements)

References 16 publications

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“…properties of an image and is similar to human visual perception (Hangarge et al, 2016). By characterising the spatial frequency structure in an image, spatial relations with the same time are provided.…”

Section: Gabor Wavelet Transformmentioning

confidence: 90%

“…In the Fourier transform, information regarding time is lost, and it is not easy to say where the exact accordance with the frequency is. However, the Gabor function simultaneously provides a resolution in both the time and frequency domains (Hangarge et al, 2016;Lee, 1996). The Gabor wavelet is an ideal basis to extract local features and it can capture directional energy features from an image (Hangarge et al, 2016).…”

Section: Gabor Wavelet Transformmentioning

confidence: 99%

“…However, the Gabor function simultaneously provides a resolution in both the time and frequency domains (Hangarge et al, 2016;Lee, 1996). The Gabor wavelet is an ideal basis to extract local features and it can capture directional energy features from an image (Hangarge et al, 2016). The Gabor wavelet method is efficient in describing the visual…”

Section: Gabor Wavelet Transformmentioning

confidence: 99%

See 2 more Smart Citations

A comparative study of different texture features for document image retrieval

Alaei

Pal

et al. 2019

Expert Systems with Applications

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Section: Gabor Wavelet Transformmentioning

confidence: 90%

Section: Gabor Wavelet Transformmentioning

confidence: 99%

Section: Gabor Wavelet Transformmentioning

confidence: 99%

See 1 more Smart Citation

A comparative study of different texture features for document image retrieval

Alaei

Pal

et al. 2019

Expert Systems with Applications

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show abstract

“…For extract features from word images, two famous features were applied. Those features which had been previously used in demonstrating handwritten words [18, 19] were selected to be used in this section. The features include local gradient histogram [20] and Gabor [19].…”

Section: Proposed Methodsmentioning

confidence: 99%

A method for handwritten word spotting based on particle swarm optimisation and multi‐layer perceptron

Tavoli

Keyvanpour

2018

IET softw.

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This study presents a new method for handwritten keyword spotting. The innovation in this paper is to provide a model based on neural network architecture and an output based on the margin. At first, a neural network is designed such that its output determines whether a test word as an input is spotted or rejected. The intended neural network has one input layer, two middle layers, and one output layer. Another innovation in this study is optimising neural network weights based on swarm optimisation method. This optimisation model is used to train the neural network, so that the output has adequate margin for classification. The new components of the proposed classifier include new particle coding and new fitness function. Two layers are considered in coding particle, one for activating and deactivating neural network nodes and the other layer for acquiring proper values for weights. Different experiments with variety of parameters were designed for the multi-layer perceptron neural network. The experiments on three datasets: AMA Arabic dataset, IAM English dataset, and IFN/Farsi dataset yielded 83, 77, and 69% values, respectively, in the best condition. The results demonstrate that the proposed method has been better than the previous ones.

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“…The reverse of the process is hypothesised to be the mechanism of visual information storage in the human brain. Hence, capsules also function as" Transforming auto encoders" [9] The authors ensure the validity of the probability factor to remain in the range of 0 and 1 with a non-linearity function which does not affect the orientation, but the magnitude is scaled down. Also, the ensurance of the output of one capsule reaching proper parent layer capsule is done by the dynamic routing algorithm.…”

Section: B Capsule Networkmentioning

confidence: 99%

Recognition of Off-line Kannada Handwritten Characters by Deep Learning using Capsule Network

G*,

Balaji,

Sharma

et al. 2019

IJEAT

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Handwritten character recognition is an important subfield of Computer Vision which has the potential to bridge the gap between humans and machines. Machine learning and Deep learning approaches to the problem have yielded acceptable results throughout, yet there is still room for improvement. off-line Kannada handwritten character recognition is another problem statement in which many authors have shown interest, but the obtained results being acceptable. The initial efforts have used Gabor wavelets and moments functions for the characters. With the introduction of Machine Learning, SVMs and feature vectors have been tried to obtain acceptable accuracies. Deep Belief Networks, ANNs have also been used claiming a con- siderable increase in results. Further advanced techniques such as CNN have been reported to be used to recognize Kannada numerals only. In this work, we budge towards solving the problem statement with Capsule Networks which is now the state of the art technology in the field of Computer Vision. We also carefully consider the drawbacks of CNN and its impact on the problem statement, which are solved with the usage of Capsule Networks. Excellent results have been obtained in terms of accuracies. We take a step further to evaluate the technique in terms of specificity, precision and f1-score. The approach has performed extremely well in terms of these measures also

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Gabor Wavelets Based Word Retrieval from Kannada Documents

Cited by 10 publications

References 16 publications

A comparative study of different texture features for document image retrieval

A comparative study of different texture features for document image retrieval

A method for handwritten word spotting based on particle swarm optimisation and multi‐layer perceptron

Recognition of Off-line Kannada Handwritten Characters by Deep Learning using Capsule Network

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