Update README.md

---
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 an optional **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, optional)*: real photo of the code (may be `null`/missing)

---

## 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.
# Depending on decoding, you can access PIL image or a dict with a local path.
render = ex["render_light"]
print(render)
```

## 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.)

## 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
@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}
}
```

README.md

@@ -1,37 +1,47 @@
----
-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: 3086674334
-    num_examples: 700
-  - name: medium
-    num_bytes: 902595780
-    num_examples: 200
-  - name: hard
-    num_bytes: 500128560
-    num_examples: 100
-  download_size: 4485774053
-  dataset_size: 4489398674
-configs:
-- config_name: default
-  data_files:
-  - split: easy
-    path: data/easy-*
-  - split: medium
-    path: data/medium-*
-  - split: hard
-    path: data/hard-*
----
+---
+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: 3086674334
+    num_examples: 700
+  - name: medium
+    num_bytes: 902595780
+    num_examples: 200
+  - name: hard
+    num_bytes: 500128560
+    num_examples: 100
+  download_size: 4485774053
+  dataset_size: 4489398674
+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
+---