Image Caption Generator

Overview of the project

We can easily identify any image immediately after seeing it, but it is hard for the computer to do the same. Nowadays, deep learning has unveiled such difficulties and has facilitated us to build an application which can identify any image. The caption of the image is based on the huge database which will be fed to the system. This machine learning project of image caption generator is implemented with the help of python language. This project will also need the techniques of convolution neural network and recurrent neural network.

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Procedure of the project

Let's understand the task first; the task is to make the computer understand the context of the image fed to it. The image should be displayed in a standard language which we can understand. This project will use a huge database; the result will be based on the manipulation of these data. For the dataset, we can download Flickr_8k for free from the internet. The advantage of using such big datasets is that we can build better models for the project. 

The flicker8K_Dataset contains all the tokens for the project. The developer should have knowledge of deep learning. Python language should also be known by the developer. The files that are to be downloaded from the internet are as follows;

• Pip install tensor flow
• Keras
• Pillow
• Numpy
• Tqdm

The CNN model is well known for its network manipulation. The images are converted into a matrix and then each value of the matrix is then compared to the dataset. The matrix is 2D and in this CNN will adhere to manipulating the pixels. The result is based on the values of the matrix saved in the dataset. Follow these steps carefully to build this project.

• First, all the files are to be imported to the project; the files will help in accessing the huge dataset.
• The second step would be to import the flicker 8k.Token file. This file contains huge data of image captions.
• The third step will be extracting the core feature of the images. For this feature we will be using the Xception model which is trained to derive the images into suitable format.
• The format will be studied by the system to give a suitable result. Then accordingly the project is trained with huge data sets.
• To make it easier for the computer to understand the English language we will be converting the words into numbers. This is done by tokenizer.p file.
• the CNN-RNN model is also defined for the project so as to do the whole process in sequence. The first is the feature extractor then sequence processor and lastly the decoder.

Conclusion

This data project will facilitate us to identify the image and give their caption. It works similarly as a camera artificial intelligence. The data set is also trained during the project, hence increasing the computer vocabulary day by day. For the project to work properly the developer should go through the basic knowledge of python language and data manipulation.

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• CNN
• Machine Learning
• Python

Image Caption Generator