Urdu Image Caption Generation
Overview
An attention-based LSTM model that generates Urdu-language captions for input images. The project required building a custom dataset of images paired with native Urdu captions, then training and deploying the model as an online service. Outcome: First-of-its-kind plug-and-play Urdu captioning model, available online for direct use.
Architecture & Pipeline
flowchart LR
n0["Image InputCustom Urdu-caption dataset"]
n1["CNN EncoderVisual features"]
n2["Attention LSTM DecoderTensorFlow"]
n3["Urdu CaptionOnline inference"]
n0 --> n1
n1 --> n2
n2 --> n3
classDef step0 fill:#f1f5f9,stroke:#64748b,color:#1e293b,stroke-width:2px,rx:10,ry:10;
classDef step1 fill:#ecfeff,stroke:#06b6d4,color:#1e293b,stroke-width:2px,rx:10,ry:10;
classDef step3 fill:#ecfdf5,stroke:#10b981,color:#1e293b,stroke-width:2px,rx:10,ry:10;
classDef step4 fill:#fffbeb,stroke:#f59e0b,color:#1e293b,stroke-width:2px,rx:10,ry:10;
class n0 step0;
class n1 step1;
class n2 step3;
class n3 step4;
End-to-end flow derived from this project's scope and tech stack. Tap View Fullscreen for a larger view, or scroll horizontally on small screens.
Key Features
- Attention-based LSTM architecture
- Custom Urdu image-caption dataset
- Trained and deployed for online inference
- Tech Stack:** Python, TensorFlow