A multi-objective approach towards cost effective isolated handwritten Bangla character and digit recognition

Sarkhel, Ritesh; Das, Nibaran; Saha, Amit; Nasipuri, Mita

doi:10.1016/j.patcog.2016.04.010

Cited by 93 publications

(36 citation statements)

References 46 publications

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“…Finally N P C * N class number of routing weights must be tuned using agreement of individual and combined digit capsules. With all these, capsule networks generally have quite slow iterations, Roy et al [12] 90.33 Pal et al [13] 93.12 Sarkhel et al [14] 86.64 but as evident from Fig. IV it also learns much faster as compared to LeNet and AlexNet.…”

Section: Results and Analysismentioning

confidence: 98%

Handwritten Indic Character Recognition using Capsule Networks

Mandal

Dubey

Ghosh

et al. 2018

2018 IEEE Applied Signal Processing Conference (ASPCON)

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Convolutional neural networks(CNNs) has become one of the primary algorithms for various computer vision tasks. Handwritten character recognition is a typical example of such task that has also attracted attention. CNN architectures such as LeNet and AlexNet have become very prominent over the last two decades however the spatial invariance of the different kernels has been a prominent issue till now. With the introduction of capsule networks, kernels can work together in consensus with one another with the help of dynamic routing, that combines individual opinions of multiple groups of kernels called capsules to employ equivariance among kernels. In the current work, we have implemented capsule network on handwritten Indic digits and character datasets to show its superiority over networks like LeNet. Furthermore, it has also been shown that they can boost the performance of other networks like LeNet and AlexNet.

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Section: Results and Analysismentioning

confidence: 98%

Handwritten Indic Character Recognition using Capsule Networks

Mandal

Dubey

Ghosh

et al. 2018

2018 IEEE Applied Signal Processing Conference (ASPCON)

Self Cite

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“…Maximum recognition accuracies of 86.6478 and 98.23% have been achieved with 0.234 and 12.60% decrease in recognition cost for handwritten Bangla characters and digits, respectively. Almost all samples were successfully recognized by Sarkhel et al (2016), despite the apparent discontinuities presented in the shapes of handwritten Bangla characters and digits.…”

Section: Handwritten Character Recognitionmentioning

confidence: 99%

“…During the past decade, we have witnessed a variety of applications of AFS theory in many fields, such as computer vision (Q. Li, Ren, Li, & Liu, 2016a;Ren, Li, Liu, & Li, 2016;Sarkhel, Das, Saha, & Nasipuri, 2016;Z. Li, Zhang, Duan, Wang, & Shi, 2018;, financial data analysis W. Tao, Liu, & Chen, 2013;Tian, Liu, & Wang, 2014;Guo, Pedrycz, & Liu, 2018), business intelligence (Bi, Cai, Liu, & Li, 2016;Xu, Liu, & Chen, 2009;Y.…”

Section: Introductionmentioning

confidence: 99%

Knowledge discovery and semantic learning in the framework of axiomatic fuzzy set theory

Liu

Jia

Wang

et al. 2018

WIREs Data Min & Knowl

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Axiomatic fuzzy set (AFS) theory facilitates a way on how to transform data into fuzzy sets (membership functions) and implement their fuzzy logic operations, which provides a flexible and powerful tool for representing human knowledge and emulate human recognition process. In recent years, AFS theory has received increasing interest. In this survey, we report the current developments of theoretical research and practical advances in the AFS theory. We first review some notion and foundations of the theory with an illustrative example, then, we focus on the various extensions of AFS theory for knowledge discovery, including clustering, classification, rough sets, formal concept analysis, and other learning tasks. Due to its unique characteristics of semantic representation, AFS theory has been applied in multiple domains, such as business intelligence, computer vision, financial analysis, and clinical data analysis. This survey provides a comprehensive view of these advances in AFS theory and its potential perspectives. This article is categorized under: Technologies > Computational Intelligence

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“…There have been different methods that are used for offline handwritten document recognition [1]. In the conventional methods with features engineered manually and using different classification algorithms to classify the characters based on the extracted features.…”

Section: Introductionmentioning

confidence: 99%

Handwritten Amharic Character Recognition System Using Convolutional Neural Networks

Abdurahman¹

2019

NWSA

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Amharic language is an official language of the federal government of the Federal Democratic Republic of Ethiopia. Accordingly, there is a bulk of handwritten Amharic documents available in libraries, information centres, museums, and offices. Digitization of these documents enables to harness already available language technologies to local information needs and developments. Converting these documents will have a lot of advantages including (i) to preserve and transfer history of the country (ii) to save storage space (ii) proper handling of documents (iv) enhance retrieval of information through internet and other applications. Handwritten Amharic character recognition system becomes a challenging task due to inconsistency of a writer, variability in writing styles of different writers, relatively large number of characters of the script, high interclass similarity, structural complexity and degradation of documents due to different reasons. In order to recognize handwritten Amharic character a novel method based on deep neural networks is used which has recently shown exceptional performance in various pattern recognition and machine learning applications, but has not been endeavoured for Ethiopic script. The Convolutional neural network model is evaluated for its performance using our database that contains 132,500 datasets of handwritten Amharic characters. Common handwritten recognition systems using machine learning use a combination of both feature extractors and classifiers. Currently the use of deep learning techniques shows promising improvements for machine learning based classification tasks. Our proposed CNN model gives an accuracy of 91.83% on training data and 90.47% on validation data.

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A multi-objective approach towards cost effective isolated handwritten Bangla character and digit recognition

Cited by 93 publications

References 46 publications

Handwritten Indic Character Recognition using Capsule Networks

Handwritten Indic Character Recognition using Capsule Networks

Knowledge discovery and semantic learning in the framework of axiomatic fuzzy set theory

Handwritten Amharic Character Recognition System Using Convolutional Neural Networks

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