--- license: cc-by-nc-4.0 task_categories: - object-detection language: - en tags: - cultural-heritage - historical-documents - synthetic-data - object-detection - biological-degradation - foxing - fungi - bacteria - yolo pretty_name: Biological Degradation Datasets for Historical Documents --- # Biological Degradation Datasets for Historical Documents These datasets were created to support research on the automatic localization of biological degradation in historical documents and were used in the experiments presented in the accompanying publication. The repository contains two complementary groups of datasets: - **Foxing and insect-induced degradation** - **Fungal and bacterial degradation** The synthetic datasets were generated using multiple compositing strategies, including direct copy-paste, alpha blending, and CMYK-based approaches. --- ## Dataset Structure ### 1. Foxing_feces This directory contains datasets derived from naturally occurring foxing stains and insect-induced degradation observed in historical documents. #### `dataset_combo_alpha` Combined dataset containing real historical document images and synthetic images generated using the **alpha-based blending approach**. #### `dataset_combo_cmyk` Combined dataset containing real historical document images and synthetic images generated using the **CMYK-based compositing approach**. #### `syn_dataset_alpha` Dataset containing only synthetic images generated using the **alpha-based blending approach**. The corresponding detector was evaluated on real historical document images. #### `syn_dataset_cmyk` Dataset containing only synthetic images generated using the **CMYK-based compositing approach**. The corresponding detector was evaluated on real historical document images. #### `real_dataset` Dataset containing only real annotated historical document images with foxing and insect-induced degradation. --- ### 2. Fungi_bacteria This directory contains synthetic datasets generated from laboratory-prepared microbial degradation samples. The source collection contains **12 microbial classes**, consisting of **6 bacterial and 6 fungal classes**: - *Epicoccum layuense* — fungus; 16 samples - *Exiguobacterium indicum* — bacterium; 20 samples - *Alternaria alternata* — fungus; 15 samples - *Aspergillus ochraceus* — fungus; 15 samples - *Talaromyces purpureogenus* — fungus; 8 samples - *Micrococcus* sp. — bacterium; 8 samples - *Sphingomonas aquatilis* — bacterium; 8 samples - *Curtobacterium flaccumfaciens* — bacterium; 7 samples - *Rhodococcus degradans* — bacterium; 7 samples - *Penicillium manginii* — fungus; 8 samples - *Arthrobacter* sp. — bacterium; 8 samples - *Cladosporium pseudocladosporioides* — fungus; 7 samples The same source stains, historical document backgrounds, deterministic generation plans, positions, scales, and rotations were used across all synthetic variants. The datasets therefore differ primarily in the compositing method applied. #### `syn_copy_paste` Synthetic dataset generated using **direct copy-paste** without additional blending. #### `syn_alpha_blend` Synthetic dataset generated using the **alpha-blending approach**. #### `syn_cmyk_blend` Synthetic dataset generated using the **CMYK-based blending approach**. #### `syn_cmyk_texture` Synthetic dataset generated using **CMYK-based blending with texture preservation**. #### `syn_cmyk_texture_feather_noise` Synthetic dataset generated using **CMYK-based blending with texture preservation, feathering, and noise-based transition masks**. --- ## Annotation Format All datasets are annotated using the **YOLO object detection format**. Each annotation file contains one object per line in the following format: ```text ``` Bounding-box coordinates are normalized to the range `[0, 1]`. --- ## Class Mapping ### Foxing_feces | Class ID | Class | |---:|---| | 0 | Foxing | | 1 | Insect-induced degradation | ### Fungi_bacteria | Class ID | Species | |---:|---| | 0 | *Sphingomonas aquatilis* | | 1 | *Rhodococcus degradans* | | 2 | *Micrococcus* sp. | | 3 | *Curtobacterium flaccumfaciens* | | 4 | *Epicoccum layuense* | | 5 | *Cladosporium pseudocladosporioides* | | 6 | *Exiguobacterium indicum* | | 7 | *Arthrobacter* sp. | | 8 | *Talaromyces purpureogenus* | | 9 | *Penicillium manginii* | | 10 | *Aspergillus ochraceus* | | 11 | *Alternaria alternata* | --- ## Experimental Use ### Dataset A – Foxing and Insect-Induced Degradation Models were trained using: - synthetic-only data - real-only data - combined real and synthetic data All models were evaluated on the same independent set of real historical document images. Two synthetic compositing approaches were compared: 1. Alpha-based blending 2. CMYK-based compositing --- ### Dataset B – Fungi and Bacteria Five synthetic compositing strategies were compared: 1. Direct copy-paste 2. Alpha blending 3. CMYK blending 4. CMYK blending with texture preservation 5. CMYK blending with texture preservation, feathering, and noise All models were trained and evaluated under the same deterministic experimental configuration. --- ## Intended Use The datasets are intended primarily for research in: - cultural heritage preservation - historical document analysis - biological degradation detection - object detection - synthetic data generation - synthetic-to-real evaluation - domain adaptation --- ## Limitations The datasets are based on a finite collection of extracted degradation samples. Although geometric transformations, different historical document backgrounds, and multiple compositing strategies increase visual variability, the generated samples remain derived from the available source stains. For the microbial benchmark, the reported test data are synthetic. Therefore, performance on these datasets should not be interpreted as direct evidence of generalization to real historical documents containing microbial degradation. An independent real-world microbial degradation dataset is required for synthetic-to-real evaluation. --- ## License This dataset is distributed under the **Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)** license. The dataset may be used for non-commercial research and academic purposes with appropriate attribution. --- ## Associated Publication This dataset accompanies the paper: **M. Grznárová et al.** *“Synthetic Dataset Generation and Object Detection Benchmarking for Biological Stain Analysis in Historical Documents”* IEEE ICCP 2026. --- ## Citation If you use this dataset in your research, please cite the dataset and the associated publication. ### Dataset citation Grznárová, M. (2026). *Biological Degradation Datasets for Historical Documents*. Hugging Face Datasets. Dataset repository: ```text https://huggingface.co/datasets/YOUR_USERNAME/YOUR_DATASET_NAME ``` ### BibTeX ```bibtex @dataset{grznarova2026biologicaldegradation, author = {Grznárová, Miriam}, title = {Biological Degradation Datasets for Historical Documents}, year = {2026}, publisher = {Hugging Face}, url = {https://huggingface.co/datasets/Melissa21/Historical-Document-Stain-Compositing-Benchmark} } ``` --- ## Contact For questions related to the dataset, please contact: **Miriam Grznárová** Faculty of Mathematics, Physics and Informatics Comenius University Bratislava, Slovakia