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To download and know more about the dataset visit:

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Cite this paper as:

Rabby A.K.M.S.A., Haque S., Islam M.S., Abujar S., Hossain S.A. (2019) Ekush: A Multipurpose and Multitype Comprehensive Database for Online Off-Line Bangla Handwritten Characters. In: Santosh K., Hegadi R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1037. Springer, Singapore


author="Rabby, A. K. M. Shahariar Azad
and Haque, Sadeka
and Islam, Md. Sanzidul
and Abujar, Sheikh
and Hossain, Sayed Akhter",
editor="Santosh, K. C.
and Hegadi, Ravindra S.",
title="Ekush: A Multipurpose and Multitype Comprehensive Database for Online Off-Line Bangla Handwritten Characters",
booktitle="Recent Trends in Image Processing and Pattern Recognition",
publisher="Springer Singapore",
abstract="Ekush the largest dataset of handwritten Bangla characters for research on handwritten Bangla character recognition. In recent years Machine learning and deep learning application-based researchers have achieved interest and one of the most significant application is handwritten recognition. Because it has the tremendous application such in Bangla OCR. Also, Bangla writing script is one of the most popular in the world. For that reason, we are introducing a multipurpose comprehensive dataset for Bangla Handwritten Characters. The proposed dataset contains Bangla modifiers, vowels, consonants, compound letters and numerical digits that consists of 367,018 isolated handwritten characters written by 3086 unique writers which were collected within Bangladesh. This dataset can be used for other problems i.e.: gender, age, district base handwritten related research, because the samples were collected include verity of the district, age group and the equal number of male and female. It is intended to fabricate acknowledgment technique for hadn written Bangla characters. This dataset is unreservedly accessible for any sort of scholarly research work. The Ekush dataset is trained and validated with EkushNet and indicated attractive acknowledgment precision 97.73{\%} for Ekush dataset, which is up until this point, the best exactness for Bangla character acknowledgment. The Ekush dataset and relevant code can be found at this link:",

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