A Hybridized Deep Learning Method for Bengali Image Captioning

An omnipresent challenging research topic in com-puter vision is the generation of captions from an input image. Previously, numerous experiments have been conducted on image captioning in English but the generation of the caption from the image in Bengali is still sparse and in need of more refining. Only a few papers till now have worked on image captioning in Bengali. Hence, we proffer a standard strategy for Bengali image caption generation on two different sizes of the Flickr8k dataset and BanglaLekha dataset which is the only publicly available Bengali dataset for image captioning. Afterward, the Bengali captions of our model were compared with Bengali captions generated by other researchers using different architectures. Additionally, we employed a hybrid approach based on InceptionResnetV2 or Xception as Convolution Neural Network and Bidirectional Long Short-Term Memory or Bidirectional Gated Recurrent Unit on two Bengali datasets. Furthermore, a different combination of word embedding was also adapted. Lastly, the performance was evaluated using Bilingual Evaluation Understudy and proved that the proposed model indeed performed better for the Bengali dataset consisting of 4000 images and the BanglaLekha dataset.

@article{Humaira2021,
  title     = {A Hybridized Deep Learning Method for Bengali Image Captioning},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {2},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Mayeesha Humaira and Shimul Paul and Md Abidur Rahman Khan Jim and Amit Saha Ami and Faisal Muhammad Shah},
  doi       = {10.14569/IJACSA.2021.0120287},
  url       = {https://doi.org/10.14569/IJACSA.2021.0120287}
}