The Yiddish Synthetic Pangoline Dataset is a comprehensive collection of synthetic Yiddish document images generated using a custom implementation of Pangoline, a text-to-image synthesis tool. The dataset contains high-quality synthetic Yiddish text rendered as images, along with corresponding ground truth text and ALTO-XML layout annotations. This dataset is designed for training and evaluating Yiddish OCR models, document layout analysis systems, and text recognition algorithms.

Dataset Details

Dataset Sources

Repository: https://huggingface.co/datasets/johnlockejrr/yid_synth_pangoline
Source Library: The Yiddish Translation of the TaNaKh by Yehoyesh Shloyme Blumgarten, Yiddish New Testament, https://github.com/New-Languages-for-NLP/yiddish
Generation Tool: Custom Pangoline implementation

Uses

Direct Use

This dataset is intended for:

OCR Model Training: Training Yiddish optical character recognition models
Document Layout Analysis: Developing systems for understanding document structure and layout
Text Recognition Research: Advancing Yiddish text recognition algorithms
Synthetic Data Studies: Research on synthetic data generation and its applications
Multilingual OCR: Cross-lingual OCR model development

Out-of-Scope Use

Commercial OCR Products: Without proper licensing verification
Biometric or Personal Identification: The dataset contains synthetic text, not real personal documents
Training Models for Deceptive Purposes: Any use intended to deceive or mislead

Dataset Structure

The dataset is organized with the following structure:

Dataset Split: train/validation/test (85%/10%/5%)
├── image: High-resolution synthetic Yiddish document images (JPG format)
├── text: Ground truth Yiddish text content (TXT format)
└── xml: ALTO-XML layout annotations (XML format)

Data Fields

image: PIL Image object containing the synthetic Yiddish document
text: String containing the ground truth Yiddish text
xml: String containing ALTO-XML formatted layout annotations

Dataset Statistics

Total Files: 75,432 samples
Training Set: 64,116 samples (85%)
Validation Set: 7,543 samples (10%)
Test Set: 3,772 samples (5%)
Total Dataset Size: 28 GB
Image Format: JPG (compressed for efficiency)
Text Encoding: UTF-8
XML Format: ALTO v4.3 standard

Dataset Creation

Curation Rationale

This dataset was created to address the scarcity of high-quality Yiddish OCR training data. Traditional OCR datasets often lack sufficient Yiddish text samples, making it challenging to develop robust Yiddish text recognition systems. By generating synthetic data using Pangoline, we can create a large-scale dataset with perfect ground truth annotations.

Source Data

Data Collection and Processing

Text Source: The Yiddish Translation of the TaNaKh by Yehoyesh Shloyme Blumgarten, Yiddish New Testament, https://github.com/New-Languages-for-NLP/yiddish
Synthetic Generation: Custom Pangoline implementation generated document images
Layout Annotation: ALTO-XML annotations were automatically generated during synthesis
Quality Control: Images were processed and optimized for training purposes
Dataset Assembly: Files were organized and split using the create_hf_dataset.py script

Who are the source data producers?

Custom Pangoline: Generated synthetic document images and layout annotations
Dataset Creator: John Locke Jr. - assembled and curated the final dataset

Annotations

Annotation process

The annotations were automatically generated during the synthetic document creation process:

Text Extraction: Ground truth text was extracted from source documents
Layout Detection: Pangoline generated precise layout information
ALTO-XML Generation: Layout data was formatted according to ALTO v4.3 standard
Coordinate Scaling: Coordinates were adjusted for optimal training

Annotation Format

The dataset uses ALTO-XML (Analyzed Layout and Text Object) format, which includes:

Page-level metadata: Dimensions, language, measurement units
Text blocks: Hierarchical organization of text content
Text lines: Individual line-level annotations
Coordinate information: Precise bounding boxes and baselines
Content strings: Actual text content for each element

Bias, Risks, and Limitations

Potential Biases

Font Bias: Synthetic generation may favor certain font styles or rendering approaches
Layout Patterns: Generated layouts may not fully represent the diversity of real Yiddish documents
Text Content: The Yiddish Translation of the TaNaKh by Yehoyesh Shloyme Blumgarten, Yiddish New Testament, https://github.com/New-Languages-for-NLP/yiddish

Risks and Limitations

Synthetic Nature: Models trained on this data may not generalize perfectly to real-world documents
Domain Specificity: Performance may vary across different document types and styles
Language Coverage: Focuses specifically on Yiddish text recognition

Recommendations

Validation: Always validate model performance on real Yiddish documents
Data Augmentation: Consider combining with real document data when available
Cross-Domain Testing: Test models across different document types and sources
Regular Updates: Consider periodic updates with new synthetic data

Citation

If you use this dataset, please cite it as follows:

@dataset{yiddish_synthetic_pangoline_2025,
  title={Yiddish Synthetic Pangoline Dataset},
  author={John Locke Jr.},
  year={2025},
  url={https://huggingface.co/datasets/johnlockejrr/yid_synth_pangoline},
  note={Synthetic Yiddish document dataset generated using custom Pangoline implementation}
}

Glossary

ALTO-XML: Analyzed Layout and Text Object XML format for describing document layout and content
Pangoline: Text-to-image synthesis tool for generating document images
OCR: Optical Character Recognition - technology for converting images of text into machine-readable text
Synthetic Data: Artificially generated data used for training machine learning models

More Information

Technical Details

Image Resolution: Optimized for OCR training (typically 1060x1500 pixels)
Text Encoding: UTF-8 with proper Yiddish character support
Coordinate System: Pixel-based coordinates in ALTO-XML format
File Organization: Each sample consists of three files with matching basenames

Usage Examples

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("johnlockejrr/yid_synth_pangoline")

# Access training data
train_data = dataset["train"]
print(f"Training samples: {len(train_data)}")

# Example usage
sample = train_data[0]
image = sample["image"]
text = sample["text"]
xml = sample["xml"]

Related Work

This dataset builds upon research in:

Synthetic data generation for OCR
Yiddish text processing and recognition
Document layout analysis
Multilingual OCR systems

Dataset Card Authors

John Locke Jr. - Dataset creator and curator

Dataset Card Contact

Repository: https://huggingface.co/datasets/johnlockejrr/yid_synth_pangoline
Issues: Please report any issues or questions through the Hugging Face dataset repository

This dataset card was created to provide comprehensive information about the Yiddish Synthetic Pangoline Dataset. For questions or contributions, please refer to the dataset repository.

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: ~75,432 document images

Creation Method

: Synthetic generation using custom Pangoline implementation