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