Recognition of Brahmi Words by Using Deep Convolutional Neural Network Article Swipe

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Neha Gautam , Soo See Chai , Jais Jose ·

YOU? · · 2020 · Open Access · · DOI: https://doi.org/10.20944/preprints202005.0455.v1

Significant progress has made in pattern recognition technology. However, one obstacle that has not yet overcome is the recognition of words in the Brahmi script, specifically the identification of characters, compound characters, and word. This study proposes the use of the deep convolutional neural network with dropout to recognize the Brahmi words. This study also proposed a DCNN for Brahmi word recognition and a series of experiments are performed on standard Brahmi dataset. The practical operation of this method was systematically tested on accessible Brahmi image database, achieving 92.47% recognition rate by CNN with dropout respectively which is among the best while comparing with the ones reported in the literature for the same task.

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Convolutional Neural Network

Artificial Intelligence

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Dropout (neural networks) Convolutional neural network Artificial intelligence Computer science Pattern recognition (psychology) Word (group theory) Task (project management) Identification (biology) Natural language processing Speech recognition Machine learning Mathematics Engineering Botany Systems engineering Geometry Biology

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Type: preprint
Language: en
Landing Page: https://doi.org/10.20944/preprints202005.0455.v1
PDF: https://www.preprints.org/manuscript/202005.0455/v1/download
OA Status: green
Cited By: 10
References: 47
Related Works: 10
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DOI: https://doi.org/10.20944/preprints202005.0455.v1

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Title: Recognition of Brahmi Words by Using Deep Convolutional Neural Network

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Type: preprint

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Language: en

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Publication year: 2020

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Publication date: 2020-05-28

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Authors: Neha Gautam, Soo See Chai, Jais Jose

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Landing page: https://doi.org/10.20944/preprints202005.0455.v1

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Concepts: Dropout (neural networks), Convolutional neural network, Artificial intelligence, Computer science, Pattern recognition (psychology), Word (group theory), Task (project management), Identification (biology), Natural language processing, Speech recognition, Machine learning, Mathematics, Engineering, Botany, Systems engineering, Geometry, Biology

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Cited by: 10

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Citations by year (recent): 2024: 1, 2023: 5, 2022: 2, 2020: 2

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