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

arXiv: https://arxiv.org/abs/2012.12139 · HTML: https://mlsanity.com/paper/2012.12139/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2020.

## Built on by (most cited first, 4 of 4)

- [ChitroJera: A Regionally Relevant Visual Question Answering Dataset for Bangla](https://mlsanity.com/paper/2410.14991/) (2024)
- [Food Recipe Recommendation Based on Ingredients Detection Using Deep Learning](https://mlsanity.com/paper/2203.06721/) (2022)
- [convoHER2: A Deep Neural Network for Multi-Stage Classification of HER2 Breast Cancer](https://mlsanity.com/paper/2211.10690/) (2022)
- [A Real-time Junk Food Recognition System based on Machine Learning](https://mlsanity.com/paper/2203.11836/) (2022)

## Abstract (arXiv)

There is very little notable research on generating descriptions of the Bengali language. About 243 million people speak in Bengali, and it is the 7th most spoken language on the planet. The purpose of this research is to propose a CNN and Bidirectional GRU based architecture model that generates natural language captions in the Bengali language from an image. Bengali people can use this research to break the language barrier and better understand each other's perspectives. It will also help many blind people with their everyday lives. This paper used an encoder-decoder approach to generate captions. We used a pre-trained Deep convolutional neural network (DCNN) called InceptonV3image embedding model as the encoder for analysis, classification, and annotation of the dataset's images Bidirectional Gated Recurrent unit (BGRU) layer as the decoder to generate captions. Argmax and Beam search is used to produce the highest possible quality of the captions. A new dataset called BNATURE is used, which comprises 8000 images with five captions per image. It is used for training 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.