Ayush-chanchal/Automated-Image-Caption-Generation-System-for-low-resource-Arabic-language

# Automated Image Caption Generation System for Low-Resource Arabic Language This repository contains the code for an **Automated Image Caption Generation System** designed for the **low-resource Arabic language**. The system uses **VGG16** for visual feature extraction and **LSTM networks** for sequence generation, producing high-quality captions in Arabic for given images. It addresses the challenge of generating coherent image captions in Arabic, a language with limited NLP resources, by combining computer vision and natural language processing. ## Table of Contents - Overview - Features - Project Architecture - Dataset - Requirements - Installation --- ## Overview The **Automated Image Caption Generation System** is a deep learning-based project aimed at generating accurate captions in Arabic for images. The model integrates **VGG16** (a convolutional neural network) for feature extraction and **LSTM** (a recurrent neural network) to generate language-based captions. This project is intended to bridge the gap in low-resource language applications by providing accurate descriptions for images, which can be useful in **assistive technology**, **content creation**, and more. ## Features - **Arabic Captioning**: Automatically generates captions in Arabic, addressing the challenges of limited resources for Arabic NLP. - **High Accuracy**: Extensive testing and validation ensure coherent, accurate captions. - **Flexible and Scalable**: Can be adapted to other low-resource languages or extended for larger datasets. - **Assistive Applications**: Useful for assistive technologies, content creation, and image-based applications. ## Project Architecture 1. **Image Feature Extraction**: The **VGG16** model is used to extract essential features from input images. 2. **Sequence Generation**: The extracted features are processed by an **LSTM network** to generate descriptive sentences in Arabic. 3. **Captioning Pipeline**: The system tokenizes and preprocesses caption …