README.md

---
dataset_info:
- config_name: default
  features:
  - name: id
    dtype: string
  - name: label
    dtype: string
  - name: image
    dtype: image
  splits:
  - name: train
    num_bytes: 12241195541
    num_examples: 601152
  - name: eval
    num_bytes: 1529748306
    num_examples: 75136
  - name: test
    num_bytes: 1533972964
    num_examples: 75168
  download_size: 15095694848
  dataset_size: 15304916811
- config_name: updated_schema
  features:
  - name: id
    dtype: string
  - name: label
    dtype: string
  - name: url
    dtype: string
  splits:
  - name: train
    num_bytes: 176527069
    num_examples: 601152
  download_size: 55583886
  dataset_size: 176527069
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: eval
    path: data/eval-*
  - split: test
    path: data/test-*
- config_name: updated_schema
  data_files:
  - split: train
    path: updated_schema/train-*
license: odc-by
task_categories:
- image-to-text
language:
- bo
tags:
- OCR
- Tibetan
- Line-to-text
size_categories:
- 100K<n<1M
---

# Dataset Card for OCR-Google_Books

A line-to-text dataset for Tibetan OCR.

## Dataset Details

### Dataset Description
- **Curated by:** Buddhist Digital Resource Center
- **Language:** Tibetan
- **Total Samples:** 751,456 line images with text transcriptions

### Dataset Structure
- **Features:**
  - `id`: Image file identifier
  - `label`: Text transcription
  - `image`: Image of a line of Tibetan text

- **Splits:**
  - **Train:** 601,152 samples (37.3M characters)
  - **Eval:** 75,136 samples (4.7M characters)  
  - **Test:** 75,168 samples (4.7M characters)

## Uses

### Direct Use
- Training and evaluation of Tibetan OCR models
- Multi-script OCR development
- Comparative analysis of modern vs. traditional printing methods
- Large-scale OCR model pretraining

### Out-of-Scope Use
- Not be suitable for handwritten Tibetan texts

## Dataset Creation

### Curation Rationale and Process
This dataset was created to support the development of robust OCR systems for Tibetan literature, encompassing both modern typography and traditional woodblock printing methods. The inclusion of multiple scripts and printing techniques makes it valuable for training models that can handle diverse Tibetan textual sources.

The dataset is constructed from Google Books scans of Tibetan texts, with Line-level image-text pairs extracted from scanned pages

## Usage

```python
from datasets import load_dataset

# Load training split
dataset = load_dataset("openpecha/OCR-Google_Books", split="train")

# Example features
print(dataset[0])
# {'id': 'I1KG1163750042_0025',
#'label':'ཡིན་པས་ཆབ་སྲིད་དང་འབྲེལ་བ་བྱུང་བ་ཙམ་ལ་ངོ་མཚར་དགོས་དོན་གང་',
#'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=860x45>}
```

## Dataset Contact
BDRC - help@bdrc.org