Abstract: Indeed, Image Captioning has become a crucial aspect of contemporary artificial intelligence because it has tackled two crucial parts of the AI field: Computer Vision and Natural Language Processing. Currently, Bangla stands as the seventh most widely spoken language globally. Due to this, image captioning has gained recognition for its significant research accomplishments. Many established datasets are found in English but no standard datasets in Bangla. For our research, we have used the BAN-Cap dataset which contains 8091 images with 40455 sentences. Many effective encoder-decoder and Visual Attention approaches are used for image captioning where CNN is utilized for the encoder and RNN is used for the decoder. However, we suggested a transformer-based image captioning model in this study with different pre-train image feature extraction models like Resnet50, InceptionV3, and VGG16 using the BAN-Cap dataset and find out its effective efficiency and accuracy based on many performances measured methods like BLEU, METEOR, ROUGE, CIDEr and also find out the drawbacks of others model.

Md. Anwar Hossain, Mirza AFM Rashidul Hasan, Ebrahim Hossen, Md Asraful, Md. Omar Faruk, AFM Zainul Abadin and Md. Suhag Ali. “Automatic Bangla Image Captioning Based on Transformer Model in Deep Learning”. International Journal of Advanced Computer Science and Applications (IJACSA) 14.11 (2023). http://dx.doi.org/10.14569/IJACSA.2023.01411113

@article{Hossain2023,
title = {Automatic Bangla Image Captioning Based on Transformer Model in Deep Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.01411113},
url = {http://dx.doi.org/10.14569/IJACSA.2023.01411113},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {11},
author = {Md. Anwar Hossain and Mirza AFM Rashidul Hasan and Ebrahim Hossen and Md Asraful and Md. Omar Faruk and AFM Zainul Abadin and Md. Suhag Ali}
}