• Salman Feroz Akhtar
• khan Monazzam
• Aishwarya Rastogi
• Samiksha Ashtikar

Captioning images automatically is one of the heart of the human visual system. There are various advantages if there is an application which automatically caption the scenes surrounded by them and revert back the caption as a plain message. In this paper, we present a model based on CNN-LSTM neural networks which automatically detects the objects in the images and generates descriptions for the images. It uses various pre-trained models to perform the task of detecting objects and uses CNN and LSTM to generate the captions. It uses Transfer Learning based pre-trained models for the task of object Detection. This model can perform two operations. The first one is to detect objects in the image using Convolutional Neural Networks and the other is to caption the images using RNN based LSTM(Long Short Term Memory). Interface of the model is developed using flask rest API, which is a web development framework of python. The main use case of this project is to help visually impaired to understand the surrounding environment and act according to that.

"DETECTING AND CAPTIONING IMAGES USING DEEP NEURAL NETWORKS AND FLASK", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 5, page no.d985-d991, May-2021, Available :http://www.jetir.org/papers/JETIR2105527.pdf

"DETECTING AND CAPTIONING IMAGES USING DEEP NEURAL NETWORKS AND FLASK", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 5, page no. ppd985-d991, May-2021, Available at : http://www.jetir.org/papers/JETIR2105527.pdf