👁️ Legend-OCR Dataset

Welcome to the Legend-OCR dataset! This is a highly robust, synthetically generated Vision dataset designed specifically for training Optical Character Recognition (OCR) models and Vision-Language Models (VLMs) like TrOCR, Florence-2, LLaVA, and Qwen-VL.

📊 Dataset Overview

• Name: Legend-OCR
• Type: Synthetic Vision-Text Pair
• Size: 100,000 High-Quality Images (Configurable)
• Format: Parquet (Embedded PNG Bytes + String Text)
• Task: Image-to-Text / OCR
• Characters Covered: Alphabets (A-Z, a-z), Numbers (0-9), and all standard Punctuation/Symbols.

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🚀 Key Features & Generation Logic

This dataset was procedurally generated using Python (PIL) with advanced randomization techniques to make the AI models robust against real-world variations:

1. Massive Font Variety: Uses multiple Linux-native fonts (Ubuntu, Roboto, Noto, Liberation) encompassing Regular, Bold, Italic, and Thin styles.
2. Dynamic Text Lengths:
   • 30% of the dataset features Single Characters (perfect for basic symbol recognition and bounding box training).
   • 70% features 10 to 20 Characters (perfect for word and sentence-level context recognition).
3. High-Contrast Backgrounds:
   • 50% Images: Dark Background with Light/White Text.
   • 50% Images: Light Background with Dark/Black Text.
4. Dynamic Image Sizing: Bounding boxes and image canvas sizes scale automatically based on text length and randomized padding, teaching the model to focus on the subject rather than a fixed aspect ratio.
5. Zero Hallucination: Since the dataset is synthetically generated natively in code, the ground truth text has a 100% accuracy rate.

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📂 Dataset Structure

Under the hood, the dataset is saved in highly compressed Parquet format. The schema looks like this:

{
  "image": {
    "bytes": "\u0089PNG\r\n\u001a\n\u0000\u0000\u0000\rIHDR...",
    "path": null
  },
  "text": "Hello@123!"
}