Datasets:
license: odc-by
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: id
dtype: string
- name: line
dtype: image
- name: transcription
dtype: string
splits:
- name: train
num_bytes: 1171331951
num_examples: 13527
download_size: 1145354398
dataset_size: 1171331951
task_categories:
- image-to-text
language:
- bo
tags:
- Tibetan
- OCR
- Line-to-Text
- Khyentse
- BDRC
size_categories:
- 10K<n<100K
Dataset Card for KhyentseWangpo
A line-to-text dataset for Tibetan OCR.
Dataset Details
Dataset Description
This dataset consists of 13,527 rows with three columns:
id(string): Unique identifier for each lineimage(image): Image file containing a line of Tibetan texttranscription(string): Tibetan text transcription in Unicode formatCurated by: Buddhist Digital Resource Center
Language: Tibetan
License: Open Data Commons Attribution License (ODC-By) v1.0
Uses
Direct Use
Training and evaluation of Tibetan OCR models, particularly for line-level recognition of modern printed texts.
Out-of-Scope Use
Not suitable for handwritten manuscripts, historical woodblock prints, or other printing styles significantly different from modern Tibetan typography.
Dataset Creation
Curation Rationale
This dataset was created to support the development of accurate OCR systems for Tibetan texts, particularly for digitizing the corpus of Tibetan Buddhist literature. The Collected Works of Jamyang Khyentse Wangpo (1820-1892), a prominent Tibetan Buddhist teacher and scholar, represents an important corpus of Tibetan religious and philosophical literature. Digitizing such works makes them more accessible to scholars, practitioners, and researchers worldwide.
Source Data
The source material consists of a modern print edition of the Collected Works (Sungbum) of Jamyang Khyentse Wangpo:
Jamyang Khyentse Wangpo. (2014). gSung ʼbum mkhyen brtseʼi dbang po (Vol. 1–25). rDzong sar blo gros phun tshogs. http://purl.bdrc.io/resource/MW3PD1002 [BDRC bdr:MW3PD1002]
The data collection process involved:
- PDF Extraction: Text was extracted from PDFs using the code found here: https://github.com/buda-base/py-tiblegenc
- Alignment: The extracted text was aligned with BDRC scans
- Line Segmentation: The scanned images and text were segmented into individual lines (13,524 total) for line-level OCR training
- Image-Text Pairing: Each line of text was paired with its corresponding image segment
Personal and Sensitive Information
This dataset contains no personal or sensitive information.