Datasets:

Nebulixlabs
/

Legend-OCR

license: apache-2.0
language:
  - en
task_categories:
  - image-to-text
tags:
  - ocr
  - synthetic
  - vision
  - trocr
  - llava
  - florence-2
  - text-recognition
  - computer-vision
size_categories:
  - 100K<n<1M
dataset_info:
  features:
    - name: image
      dtype: image
    - name: text
      dtype: string
  splits:
    - name: train
      num_bytes: 556311460
      num_examples: 100000
  download_size: 557311624
  dataset_size: 556311460
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

👁️ 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.

🚀 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:

Massive Font Variety: Uses multiple Linux-native fonts (Ubuntu, Roboto, Noto, Liberation) encompassing Regular, Bold, Italic, and Thin styles.
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).
High-Contrast Backgrounds:
- 50% Images: Dark Background with Light/White Text.
- 50% Images: Light Background with Dark/Black Text.
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.
Zero Hallucination: Since the dataset is synthetically generated natively in code, the ground truth text has a 100% accuracy rate.

📂 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!"
}