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ABSTRACT
The combination of computer vision and natural language processing in Artificial intelligence has sparked a lot of interest in research in recent years, thanks to the advent of deep learning. The context of a photograph is automatically described in English. When a picture is captioned, the computer learns to interpret the visual information of the image using one or more phrases. The ability to analyze the state,  properties, and relationship between these objects is required for the meaningful description generation process of high-level picture semantics. Using CNN -LSTM architectural models on the captioning of a graphical image, we hope to detect things and inform people via text messages in this research. To correctly identify the items,  the input image is first reduced to grayscale and then processed by a Convolution Neural Network (CNN). The COCO Dataset 2017 was used. The proposed method for blind individuals is intended to be expanded to include persons with vision loss to speech messages to help them reach their full potential and to track their intellect. In this project, we follow a variety of important concepts of image captioning and its standard processes, as this work develops a generative CNN-LSTM model that outperforms human baselines.

Abstract:

This undergraduate project, “Image Caption Generator using Deep Learning,” aims to develop a sophisticated system capable of generating descriptive captions for images. By integrating Python and web technologies, the project utilizes deep learning techniques to bridge the gap between computer vision and natural language processing, enabling the automatic creation of meaningful captions for images.

Existing System:

Current image captioning systems often rely on traditional computer vision methods and rule-based approaches, limiting their ability to generate contextually rich and diverse captions. The need for a system that leverages deep learning to understand and describe image content more intuitively is essential for advancing the field.

Proposed System:

The proposed system introduces an innovative Image Caption Generator using Deep Learning. It employs Convolutional Neural Networks (CNNs) for image feature extraction and Recurrent Neural Networks (RNNs) for sequence generation, enabling the generation of accurate and contextually relevant captions for a wide range of images.

System Requirements:

• Python programming language
• Web server (e.g., Flask or Django)
• Database server (e.g., SQLite or PostgreSQL)
• Libraries: TensorFlow, Keras, NLTK (Natural Language Toolkit), Pillow
• Pre-trained CNN models (e.g., VGG16 or ResNet) for image feature extraction

Algorithms:

The system incorporates CNNs to extract visual features from images and an LSTM (Long Short-Term Memory) network for generating coherent and contextually rich captions. The training process involves optimizing the model using techniques like transfer learning to enhance the accuracy and diversity of generated captions.

Hardware and Software Requirements:

• Hardware: GPU for accelerated deep learning computations
• Software: Python, Web server, Database management system, TensorFlow, Keras

Architecture:

The system follows a client-server architecture, with the server handling image processing, deep learning tasks, and database management. The frontend, designed for user interaction, communicates with the backend through RESTful APIs. The architecture ensures scalability and responsiveness for generating captions in real-time.

Technologies Used:

• Python: Core programming language
• Web server (e.g., Flask or Django): Backend web framework
• Database management system (e.g., SQLite or PostgreSQL): Data storage
• TensorFlow, Keras: Deep learning frameworks
• NLTK: Natural language processing toolkit
• Pillow: Image processing library
• Git: Version control for collaborative development

Web User Interface:

The web interface provides users with the capability to upload images and receive dynamically generated captions. Users can visualize the images alongside their respective captions, fostering a seamless and interactive experience. The interface is designed to be user-friendly, making image captioning accessible to a broad audience.

In conclusion, the “Image Caption Generator using Deep Learning” undergraduate project offers an advanced solution for generating contextually relevant captions for images. By leveraging deep learning algorithms and web technologies, the system aims to provide a valuable tool for content creators, photographers, and enthusiasts looking to enhance the descriptive content associated with their images.

UML DIAGRAMS

Collaboration Diagram

Architecture diagram

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use case diagram

activity diagram

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Deployment Diagram

Flow Chart Diagram