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--- |
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language: |
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- fa |
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pretty_name: Persian Historical Documents Handwritten Characters |
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size_categories: |
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- 1K<n<10K |
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tags: |
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- ocr |
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- character-recognition |
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- persian |
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- historical |
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- handwritten |
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- nastaliq |
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- character |
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--- |
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# Persian Historical Documents Handwritten Characters |
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## Dataset Description |
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- **Model**: https://huggingface.co/iarata/Few-Shot-PHCR |
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- **Repository:** https://github.com/iarata/persian-docs-ocr |
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- **Paper:** https://doi.org/10.1007/978-3-031-53969-5_20 |
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- **Point of Contact:** hajebrahimi.research [at] gmail [dot] com |
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### Summary |
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This dataset contains pre-processed images of Persian characters' contextual forms (except letter گ) from 5 handwritten Persian historical books written in Nastaliq script. The dataset contains 2775 images of 111 classes. The images are in TIFF format and have a resolution of 72 dpi. The images are in black and white and have a size of 395 × 395 pixels. |
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### Languages |
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Persian |
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## Dataset Structure |
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The dataset is structured as follows: |
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``` |
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├── data |
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│ ├── 06a9_01.tif |
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│ ├── 06a9_02.tif |
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│ ├── 06a9_03.tif |
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│ ├── 06a9_04.tif |
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│ ├── 06a9_05.tif |
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│ ├── ... |
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│ ├── 06a9_25.tif |
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│ │ |
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│ ├── 06cc_01.tif |
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│ ├── 06cc_02.tif |
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│ ├── 06cc_03.tif |
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│ ├── 06cc_04.tif |
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│ ├── 06cc_05.tif |
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│ ├── ... |
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│ ├── 06cc_25.tif |
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│ ├── ... |
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``` |
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The naming of each image indicates the UTF-16 hexadecimal code ([Hex to String Decoder](https://dencode.com/en/string/hex)) of a character's contextual form followed by the number of the image. In the numbering, every 5 images are from a new book. The contextual form of every character is treated as a separate class resulting in 111 classes. |
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## Dataset Creation |
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For building this dataset 5 historical Persian books from the [Library of Congress](loc.gov) |
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### Source Data |
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The data was collected from 5 historical Persian books from the [Library of Congress](loc.gov). The books are as follows: |
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- [Shah-nameh by Firdausi](https://www.loc.gov/item/2012498868/) |
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- [Dīvān](https://www.loc.gov/item/2015481730/) |
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- [Kitāb-i Rūmī al-Mawlawī](https://www.loc.gov/item/2016397707) |
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- [Gulistān](https://www.loc.gov/item/2017406684/) |
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- [Qajar-era poetry](https://www.loc.gov/item/2017498320/) |
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The images were pre-processed using the following steps: |
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Images were first normalized to reduce noise from the background of the characters. The normalized image is then converted to a single-channel grayscale image. Following that, image thresholding is applied to the grayscale image to remove the characters' background. The thresholded image is binarized so that the pixel values greater than 0 become 255 (white), and pixels with a value of 0 (black) remain unchanged. Finally, the binarized image is inversed. |
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### Annotations |
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Before pre-processing the images the characters were cropped from the books and were saved with their UTF-16 hexadecimal code plus the number of the image (e.g. 06a9_01.tif). |
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#### Annotators: |
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- [Hajebrahimi Alireza](https://www.linkedin.com/in/alireza-hajebrahimi/) |
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- [Hajebrahimi Reyhaneh](https://www.linkedin.com/in/reyhaneh-hajebrahimi-2565451a0/) |
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### Citation Information |
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Hajebrahimi, A., Santoso, M.E., Kovacs, M., Kryssanov, V.V. (2024). Few-Shot Learning for Character Recognition in Persian Historical Documents. In: Nicosia, G., Ojha, V., La Malfa, E., La Malfa, G., Pardalos, P.M., Umeton, R. (eds) Machine Learning, Optimization, and Data Science. LOD 2023. Lecture Notes in Computer Science, vol 14505. Springer, Cham. https://doi.org/10.1007/978-3-031-53969-5_20 |
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**BibTeX:** |
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```bibtex |
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@InProceedings{10.1007/978-3-031-53969-5_20, |
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author="Hajebrahimi, Alireza |
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and Santoso, Michael Evan |
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and Kovacs, Mate |
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and Kryssanov, Victor V.", |
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editor="Nicosia, Giuseppe |
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and Ojha, Varun |
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and La Malfa, Emanuele |
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and La Malfa, Gabriele |
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and Pardalos, Panos M. |
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and Umeton, Renato", |
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title="Few-Shot Learning for Character Recognition in Persian Historical Documents", |
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booktitle="Machine Learning, Optimization, and Data Science", |
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year="2024", |
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publisher="Springer Nature Switzerland", |
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address="Cham", |
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pages="259--273", |
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abstract="Digitizing historical documents is crucial for the preservation of cultural heritage. The digitization of documents written in Perso-Arabic scripts, however, presents multiple challenges. The Nastaliq calligraphy can be difficult to read even for a native speaker, and the four contextual forms of alphabet letters pose a complex task to current optical character recognition systems. To address these challenges, the presented study develops an approach for character recognition in Persian historical documents using few-shot learning with Siamese Neural Networks. A small, novel dataset is created from Persian historical documents for training and testing purposes. Experiments on the dataset resulted in a 94.75{\%} testing accuracy for the few-shot learning task, and a 67{\%} character recognition accuracy was observed on unseen documents for 111 distinct character classes.", |
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isbn="978-3-031-53969-5" |
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} |
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``` |
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