---
dataset_info:
  features:
  - name: id
    dtype: string
  - name: difficulty
    dtype: string
  - name: code
    dtype: string
  - name: render_light
    dtype: image
  - name: render_dark
    dtype: image
  - name: photo
    dtype: image
  splits:
  - name: easy
    num_bytes: 3082543834
    num_examples: 700
  - name: medium
    num_bytes: 902595780
    num_examples: 200
  - name: hard
    num_bytes: 500128560
    num_examples: 100
  download_size: 4481644051
  dataset_size: 4485268174
configs:
- config_name: default
  data_files:
  - split: easy
    path: data/easy-*
  - split: medium
    path: data/medium-*
  - split: hard
    path: data/hard-*
license: mit
task_categories:
- image-to-text
- text-generation
tags:
- code
- ocr
pretty_name: CodeOCR
size_categories:
- 1K<n<10K
---

---
pretty_name: "CodeOCR Dataset (Python Code Images + Ground Truth)"
license: mit
language:
  - en
task_categories:
  - image-to-text
tags:
  - ocr
  - code
  - python
  - leetcode
  - synthetic
  - computer-vision
size_categories:
  - 1K<n<10K
---

# CodeOCR Dataset (Python Code Images + Ground Truth)

This dataset is designed for **Optical Character Recognition (OCR) of source code**.  
Each example pairs **Python code (ground-truth text)** with **image renderings** of that code (light/dark themes) and a **real photo**.

## Dataset Summary

- **Language:** Python (text ground truth), images of code
- **Splits:** `easy`, `medium`, `hard`
- **Total examples:** 1,000  
  - `easy`: 700  
  - `medium`: 200  
  - `hard`: 100  
- **Modalities:** image + text

### What is “ground truth” here?

The `code` field is **exactly the content of `gt.py`** used to generate the synthetic renderings.  
During dataset creation, code is normalized to ensure stable GT properties:

- UTF-8 encoding  
- newline normalization to **LF (`\n`)**  
- tabs expanded to **4 spaces**  
- syntax checked with Python `compile()` (syntax/indentation correctness)

This makes the dataset suitable for training/evaluating OCR models that output **plain code text**.

---

## Data Fields

Each row contains:

- `id` *(string)*: sample identifier (e.g., `easy_000123`)
- `difficulty` *(string)*: `easy` / `medium` / `hard`
- `code` *(string)*: **ground-truth Python code**
- `render_light` *(image)*: synthetic rendering (light theme)
- `render_dark` *(image)*: synthetic rendering (dark theme)
- `photo` *(image)*: real photo of the code

---

## How to Use

### Load with 🤗 Datasets

```python
from datasets import load_dataset

ds = load_dataset("maksonchek/codeocr-dataset")
print(ds)
print(ds["easy"][0].keys())
```

### Access code and images

```python
ex = ds["easy"][0]

# Ground-truth code
print(ex["code"][:500])

# Images are stored as `datasets.Image` features.
render = ex["render_light"]
print(render)
```

If your environment returns image dicts with local paths:

```python
from PIL import Image

img = Image.open(ex["render_light"]["path"])
img.show()
```

Real photo (always present in this dataset):

```python
from PIL import Image

photo = Image.open(ex["photo"]["path"])
photo.show()
```

---

## Dataset Creation

### 1) Code selection
Python solutions were collected from an open-source repository of LeetCode solutions (MIT licensed).

### 2) Normalization to produce stable GT
The collected code is written into `gt.py` after:

- newline normalization to LF
- tab expansion to 4 spaces
- basic cleanup (no hidden control characters)
- Python syntax check via `compile()`

### 3) Synthetic rendering
Synthetic images are generated from the normalized `gt.py` in:

- light theme (`render_light`)
- dark theme (`render_dark`)

### 4) Real photos
Real photos are manually captured and linked **for every sample**.

---

## Statistics (high-level)

Average code length by difficulty (computed on this dataset):

- `easy`: ~27 lines, ~669 chars
- `medium`: ~36 lines, ~997 chars
- `hard`: ~55 lines, ~1767 chars

(Exact values may vary if the dataset is extended.)

---

## Intended Use

- OCR for programming code
- robust text extraction from screenshot-like renders and real photos
- benchmarking OCR pipelines for code formatting / indentation preservation

### Not Intended Use

- generating or re-distributing problem statements
- competitive programming / cheating use-cases

---

## Limitations

- Code is checked for **syntax correctness**, but not necessarily for runtime correctness.
- Rendering style is controlled and may differ from real-world photos.

---

## License & Attribution

This dataset is released under the **MIT License**.

The included solution code is derived from **kamyu104/LeetCode-Solutions** (MIT License):  
https://github.com/kamyu104/LeetCode-Solutions

If you use this dataset in academic work, please cite the dataset and credit the original solution repository.

---

## Citation

### BibTeX

```bibtex
@dataset{codeocr_leetcode_2025,
  author       = {Maksonchek},
  title        = {CodeOCR Dataset (Python Code Images + Ground Truth)},
  year         = {2025},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/maksonchek/codeocr-dataset}
}
```