English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Optical character recognition (OCR) is a process of converting analoguedocuments into digital using document images. Currently, many commercial andnon-commercial OCR systems exist for both handwritten and printed copies fordifferent languages. Despite this, very few works are available in case ofrecognising Bengali words. Among them, most of the works focused on OCR ofprinted Bengali characters. This paper introduces an end-to-end OCR system forBengali language. The proposed architecture implements an end to end strategythat recognises handwritten Bengali words from handwritten word images. Weexperiment with popular convolutional neural network (CNN) architectures,including DenseNet, Xception, NASNet, and MobileNet to build the OCRarchitecture. Further, we experiment with two different recurrent neuralnetworks (RNN) methods, LSTM and GRU. We evaluate the proposed architectureusing BanglaWritting dataset, which is a peer-reviewed Bengali handwrittenimage dataset. The proposed method achieves 0.091 character error rate and0.273 word error rate performed using DenseNet121 model with GRU recurrentlayer.

End-to-End Optical Character Recognition for Bengali Handwritten Words