| | --- |
| | license: mit |
| | language: |
| | - am |
| | size_categories: |
| | - 100K<n<1M |
| | --- |
| | # Fidel: A Large-Scale Sentence Level Amharic OCR Dataset |
| |
|
| | ## Overview |
| |
|
| | Fidel is a comprehensive dataset for Amharic Optical Character Recognition (OCR) at the sentence level. It contains a diverse collection of Amharic text images spanning handwritten, typed, and synthetic sources. This dataset aims to advance language technology for Amharic, serving critical applications such as digital ID initiatives, document digitization, and automated form processing in Ethiopia. |
| |
|
| | ## Citation |
| |
|
| | If you use the **Fidel: A Large-Scale Sentence Level Amharic OCR Dataset** in your work, please cite the associated pre-print using the following BibTeX entry: |
| |
|
| | ```bibtex |
| | @article{Fidel2025, |
| | author = {Chamisso, Tunga Tessema and Guda, Blessed and Adego, Bereket Retta and Sagbo, Carmel Prosper and Ashungafac, Gabrial Zencha and Gueye, Assane}, |
| | title = {{Fidel: A Large-Scale Sentence Level Amharic OCR Dataset}}, |
| | journal = {Research Square}, |
| | year = {2025}, |
| | doi = {10.21203/rs.3.rs-8118465/v1}, |
| | url = {[https://doi.org/10.21203/rs.3.rs-8118465/v1](https://doi.org/10.21203/rs.3.rs-8118465/v1)} |
| | } |
| | ``` |
| | ## Dataset Structure |
| |
|
| | The dataset is organized into train and test splits: |
| |
|
| | ``` |
| | fidel-dataset/ |
| | ├── train/ # training images (handwritten, typed, and synthetic) |
| | ├── test/ # test images (handwritten, typed, and synthetic) |
| | └── metadata.json # Croissant metadata file |
| | ├── train_labels.json # Contains filenames and corresponding text labels |
| | └── test_labels.json # Contains filenames and corresponding text labels |
| | ``` |
| |
|
| | ### Labels Format |
| |
|
| | Each CSV file contains the following columns: |
| | - `image_filename`: Name of the image file |
| | - `line_text`: The Amharic text content in the image |
| | - `type`: The source type (handwritten, typed, or synthetic) |
| | - `writer`: The writer number (for handwritten types only) |
| |
|
| | #### Example Labels |
| |
|
| | | image_filename | text |
| | |---|---| |
| | | 25_line_4.png | ዲግሪዎች የህዝብ አስተዳደር ትምህርት ተምረው አንዳገኟቸው ሲገልጹ የቆዩ ሲሆን ይህንንም በፓርላማ ድረገጽ ፣ በፌስቡክ ፣ ዊኪፔድያ ፣ |
| | | 3_line_2.png | ዮርክ ኬኔዲ አየር ጣቢያ ተነስቶ ሎንዶን ሂዝሮው አየር ጣቢያ አረፈ። ዝምባብዌም በመንግስት ለታገዘ ዝርፊያ እንዲሁም ለድህነትና በሽታ እጇን |
| | |
| | |
| | ## Usage |
| | |
| | ``` |
| | # Install git-lfs if not already installed |
| | git lfs install |
| | |
| | # Clone with LFS support for large files |
| | git clone https://huggingface.co/datasets/upanzi/fidel-dataset |
| | cd fidel-dataset |
| | |
| | # Pull LFS files (zip archives) |
| | git lfs pull |
| | |
| | # Extract the archives |
| | unzip train.zip |
| | unzip test.zip |
| | ``` |
| | ## Dataset Statistics |
| | |
| | ### Overall Statistics |
| | - Total samples: 193,185 |
| | - Training samples: 169,499 |
| | - Test samples: 23,686 |
| | |
| | ### By Source Type |
| | - Handwritten: 40,265 samples |
| | - Typed: 28,342 samples |
| | - Synthetic: 61,530 samples |
| | - HDD: 63,048 samples |
| | |
| | ### Image Characteristics |
| | - Average image width: varies by type (handwritten: 2,480px, typed: 2,482px, synthetic: 2,956px) |
| | - Average image height: varies by type (handwritten: 199px, typed: 71px, synthetic: 244px) |
| | - Average aspect ratio: varies by type (handwritten: 14.0, typed: 19.5, synthetic: 11.6) |
| | |
| | ### Text Characteristics |
| | - Average text length: varies by type (handwritten: 62.0 characters, typed: 95.2 characters, synthetic: 74.7 characters) |
| | - Average word count: varies by type (handwritten: 11.3 words, typed: 16.9 words, synthetic: 14.7 words) |
| | - Unique characters: 249 in handwritten, 200 in typed, 190 in synthetic |
| | |
| | ## License |
| | |
| | This dataset is released under: [MIT License](https://opensource.org/licenses/MIT) |
| | |
| | ## Acknowledgments |
| | |
| | We thank all contributors who provided handwritten samples and the organizations that supported this data collection effort. |