Text to Picto Generation – ImageCLEF Challenge

Introduction

This project presents a text-to-pictogram generation system developed as part of the ImageCLEF Challenge, a recognized evaluation benchmark focused on multimodal and multilingual information retrieval tasks.

The objective of the system is to transform natural language text descriptions into corresponding pictogram-style images, enabling visual representation of textual content. Such systems are particularly useful in accessibility-driven applications, educational tools, and assistive communication technologies.

Tech Stack

• Programming Language: Python
• Development Environment: Jupyter Notebook
• Libraries and Frameworks:
  • NumPy
  • Pandas
  • OpenCV / PIL
  • Natural Language Processing libraries
  • Deep Learning frameworks (TensorFlow / PyTorch)
• Domain: Multimodal Machine Learning (Text and Image)

Features

• Converts textual input into pictogram-style visual representations
• Performs text preprocessing and feature extraction
• Maps semantic text representations to visual concepts
• Supports experimentation aligned with ImageCLEF challenge guidelines
• Modular pipeline design for extensibility and analysis

Process Workflow

1. Text Preprocessing

   • Cleaning and normalization of raw input text
   • Tokenization and semantic feature extraction

2. Representation Learning

   • Encoding text into numerical embeddings
   • Learning associations between textual and visual representations

3. Pictogram Generation

   • Image generation or retrieval based on learned mappings
   • Post-processing to ensure pictogram-style consistency

4. Evaluation

   • Qualitative assessment of generated pictograms
   • Analysis based on challenge-specific objectives

Output Example

A sample output generated by the system is shown below.

What I Learned

• Practical implementation of multimodal machine learning pipelines
• Working with benchmark datasets and task definitions
• Integration of NLP and computer vision techniques
• Structuring experiments for reproducibility and clarity
• Presenting technical work through both code and documentation

Future Scope

• Adoption of transformer-based multimodal architectures
• Support for multilingual text inputs
• Deployment as a web-based demonstration application
• Inclusion of quantitative evaluation metrics for performance comparison

Repository Contents

• Text_to_Picto_ImageCLEF.ipynb – Jupyter notebook containing the implementation
• Presentation_ImageCLEF.pptx – Project presentation used for evaluation and explanation
• outputs/ – Sample generated pictogram outputs

References

• ImageCLEF Benchmark Tasks and Evaluation Framework