Project Title: Image Captioning Using Deep Learning

Objective:

To generate descriptive natural language captions for input images by combining computer vision and natural language processing (NLP).

Dataset:

COCO (Common Objects in Context) or Flickr8k/Flickr30k datasets.

Each image is paired with 5 human-written captions describing the scene.

Key Steps:

Data Preprocessing:

Images: Resize, normalize, and extract features using a pre-trained CNN (e.g., InceptionV3, ResNet).

Captions: Tokenize, remove punctuation, add start/end tokens, pad sequences.

Model Architecture:

Encoder: A CNN (e.g., InceptionV3) extracts feature vectors from images.

Decoder: An RNN (typically LSTM or GRU) generates captions word by word based on the encoded image features.

Use Attention Mechanism to improve context awareness during caption generation.

Training:

Use image features and partial captions to predict the next word in the sequence.

Loss function: usually categorical cross-entropy.

Evaluation:

Automatic metrics: BLEU, METEOR, ROUGE, CIDEr scores.

Qualitative analysis: human judgment on caption quality and relevance.

Deployment:

Build a web or mobile app that lets users upload images and receive generated captions.

Use TensorFlow, Flask, or Streamlit for integration.

Tools & Libraries:

Python, NumPy, Pandas

TensorFlow/Keras or PyTorch

NLTK/spaCy for text processing

Matplotlib/OpenCV for image handling

Applications:

Assistive tech for visually impaired users

Automated content creation

Image indexing in large databases

E-commerce and social media tagging