A DHCR_ SmartNet: A smart Devanagari handwritten character recognition using level-wised CNN architecture

• Autorzy: Deore Shalaka Prasad

Identyfikatory

DOI

10.7494/csci.2022.23.3.4487

• Języki publikacji: EN

Abstrakty

EN

Handwritten script recognition is a vital application of the machine-learning domain. Applications like automatic license plate detection, pin-code detection, and historical document management increases attention toward handwritten script recognition. English is the most widely spoken language in India; hence, there has been a lot of research into identifying a script using a machine. Devanagari is a popular script that is used by a large number of people on the Indian subcontinent. In this paper, a level-wised efficient transfer-learning approach is presented on the VGG16 model of a convolutional neural network (CNN) for identifying isolated Devanagari handwritten characters. In this work, a new dataset of Devanagari characters is presented and made accessible to the public. This newly created dataset is comprised of 5800 samples for 12 vowels, 36 consonants, and 10 digits. Initially, a simple CNN is implemented and trained on this new small dataset. During the next stage, a transfer-learning approach is implemented on the VGG16 model, and during the last stage, the efficient fine-tuned VGG16 model is implemented. The obtained accuracy of the fine-tuned model’s training and testing came to 98.16% and 96.47%, respectively.

Słowa kluczowe

EN

convolutional neural network; VGG16; fine-tuned; handwritten script; Devanagari characters

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Science
• Rocznik: 2022
• Tom: T. 23 (3)
• Strony: 301--320
• Opis fizyczny: Bibliogr. 37 poz., rys., tab.

Twórcy

autor

Deore Shalaka Prasad

• S.P. Pune University, Department of Computer Engineering, M.E.S. College of Engineering, Pune, Maharashtra, India

• https://orcid.org/0000-0002-6767-6289

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).