• Hozefa Abbas Arielwala
• Salik Zulfiqar Ahmed Ansari
• Mohd Eklaque Shah
• Shaikh Mohd Ashfaque

Presently, visually impaired individuals carry sticks with them which helps them to scan their surroundings for obstacles or orientation marks and to measure the distance between them and objects in their surroundings. If the visually impaired people are provided with an audio description of their surroundings, it would have a significant effect on their lives and help them understand their surroundings better. The recent advances in Deep Learning and Computer Vision have led to excellent Image Captioning models using advanced techniques like Deep Reinforcement Learning. These captions can then be read out loud to the visually impaired so that they can have a better understanding of their surroundings. Our model uses a convolutional neural network (CNN) to extract features from an image which is then converted into a sentence, describing the image invalid English by feeding it to a recurrent neural network or a Long Short-Term Memory network. We believe that our model with greatly improve the life of visually impaired people by allowing them to understand their surroundings.

Android application, visually impaired person , deep learning, CNN , Convolutional neural network , image recognition , object recognition , object detection , NLP , RNN

"An Android Application for Visually Impaired Person Using Deep learning. ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 10, page no.e499-e506, October-2021, Available :http://www.jetir.org/papers/JETIR2110476.pdf

"An Android Application for Visually Impaired Person Using Deep learning. ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 10, page no. ppe499-e506, October-2021, Available at : http://www.jetir.org/papers/JETIR2110476.pdf