Datasets:
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---
dataset_info:
features:
- name: filename
dtype: string
- name: label
dtype: string
- name: url
dtype: string
- name: BDRC_work_id
dtype: string
- name: char_len
dtype: int64
- name: script
dtype: string
- name: print_method
dtype: string
splits:
- name: train
num_bytes: 210467728
num_examples: 601152
- name: eval
num_bytes: 26280512
num_examples: 75136
- name: test
num_bytes: 26308535
num_examples: 75168
download_size: 76386563
dataset_size: 263056775
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: eval
path: data/eval-*
- split: test
path: data/test-*
---
# 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
- `url`: Source URL of the original document
- **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
- May not suitably represent contemporary digital Tibetan fonts
## 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་',
# 'url': 'https://s3.amazonaws.com/monlam.ai.ocr/OCR/training_images/I1KG1163750042_0025.jpg'}
```
## Dataset Contact
BDRC - help@bdrc.org |