A Simplistic & Fast approach to Bangla Handwritten Digit Recognition using Convolutional Neural Network

Emon, Eksan Ahmed; Adil, Ifta Khairul; Ahbab, Abir

dc.contributor.author	Emon, Eksan Ahmed
dc.contributor.author	Adil, Ifta Khairul
dc.contributor.author	Ahbab, Abir
dc.date.accessioned	2022-04-16T02:19:10Z
dc.date.available	2022-04-16T02:19:10Z
dc.date.issued	2021-03-30
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dc.identifier.uri	http://hdl.handle.net/123456789/1314
dc.description	Supervised by Dr.Hasanul Kabir Professor, and Co-Supervisor, Sabbir Ahmed Lecturer Department of Computer Science and Engineering (CSE), Islamic University of Technology (IUT), OIC	en_US
dc.description.abstract	Handwritten digit recognition has consistently a major test because of its variety of shape, size, and composing style.Accurate Handwritten Digit Recognition is becoming challenging and thoughtful to researchers due to its educational and economic values.Most of the To benchmark Bengali digit acknowledgment calculations, a huge openly accessible dataset is required which is liberated from inclinations starting from topographical area, sexual orientation, and age.In light of this point, NumtaDB, a dataset comprising of something else than 85,000 pictures of transcribed Bengali digits, has been amassed.The challenges of NumtaDB data set is that it contains unbiased, unprocessed and augmented images.So for this reason different kinds preprocessing steps were followed to process the available data and A simplistic fast approach to Bangla Handwritten digit recognition using Convolution Neural Network is proposed.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur, Bangladesh	en_US
dc.subject	Convolutional Neural Network, handwritten digit recognition, Bangla handwritten digits, Image Preprocessing	en_US
dc.title	A Simplistic & Fast approach to Bangla Handwritten Digit Recognition using Convolutional Neural Network	en_US
dc.type	Thesis	en_US