People who suffer from low vision, sight and visual impairment are not able to see words and letters in ordinary newsprint, books and magazines clearly. This can make the reading process difficult which can disturb the learning process and slow the person's intelligence development. Therefore, a device is needed to help them read. So we had developed one such device that can scan and read any kind of text by changing it to voice message. The purpose of this device is to process the input Image, pdf, Documents, Textbooks, and News Papers as input into a voice as output. Captioning an image involves generating a human readable textual description given an image, such as a photograph. It is an easy problem for a human, but very challenging for a machine as it involves both understanding the content of an image and how to translate this understanding into natural language. We create an automated image captioning model using Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to produce a series of texts that best describe the image and then those captions are given as input for text to audio conversion.
Understand the theory and intuition behind Convolutional Neural Networks (CNNs).
Build and train a Convolutional Neural Network using Keras with Tensorflow 2.0 as a backend.
Assess the performance of trained CNN and ensure its generalization using various Key performance indicators.
