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There is very little notable research on generating descriptions of theBengali language. About 243 million people speak in Bengali, and it is the 7thmost spoken language on the planet. The purpose of this research is to proposea CNN and Bidirectional GRU based architecture model that generates naturallanguage captions in the Bengali language from an image. Bengali people can usethis research to break the language barrier and better understand each other'sperspectives. It will also help many blind people with their everyday lives.This paper used an encoder-decoder approach to generate captions. We used apre-trained Deep convolutional neural network (DCNN) called InceptonV3imageembedding model as the encoder for analysis, classification, and annotation ofthe dataset's images Bidirectional Gated Recurrent unit (BGRU) layer as thedecoder to generate captions. Argmax and Beam search is used to produce thehighest possible quality of the captions. A new dataset called BNATURE is used,which comprises 8000 images with five captions per image. It is used fortraining and testing the proposed model. We obtained BLEU-1, BLEU-2, BLEU-3,BLEU-4 and Meteor is 42.6, 27.95, 23, 66, 16.41, 28.7 respectively.

Image to Bengali Caption Generation Using Deep CNN and Bidirectional Gated Recurrent Unit