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PHCR_DB25_2775.zip

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README.md

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+---
+language:
+- fa
+pretty_name: Persian Historical Documents Handwritten Characters
+size_categories:
+- 1K<n<10K
+tags:
+- ocr
+- character-recognition
+- persian
+- historical
+- handwritten
+- nastaliq
+- character
+---
+
+# Persian Historical Documents Handwritten Characters 
+
+## Dataset Description
+
+- **Repository:** https://github.com/iarata/persian-docs-ocr
+- **Paper:** https://doi.org/10.1007/978-3-031-53969-5_20
+- **Point of Contact:**  hajebrahimi.research [at] gmail [dot] com
+
+### Summary
+
+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. 
+
+### Languages
+
+Persian
+
+![Sample view of the dataset](dataset-sample-view.png)
+
+## Dataset Structure
+
+The dataset is structured as follows:
+
+```
+├── data
+│   ├── 06a9_01.tif
+│   ├── 06a9_02.tif
+│   ├── 06a9_03.tif
+│   ├── 06a9_04.tif
+│   ├── 06a9_05.tif
+│   ├── ...
+│   ├── 06a9_25.tif
+│   │
+│   ├── 06cc_01.tif
+│   ├── 06cc_02.tif
+│   ├── 06cc_03.tif
+│   ├── 06cc_04.tif
+│   ├── 06cc_05.tif
+│   ├── ...
+│   ├── 06cc_25.tif
+│   ├── ...
+```
+
+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.
+
+## Dataset Creation
+
+For building this dataset 5 historical Persian books from the [Library of Congress](loc.gov)
+
+### Source Data
+
+
+The data was collected from 5 historical Persian books from the [Library of Congress](loc.gov). The books are as follows:
+
+- [Shah-nameh by Firdausi](https://www.loc.gov/item/2012498868/)
+- [Dīvān](https://www.loc.gov/item/2015481730/)
+- [Kitāb-i Rūmī al-Mawlawī](https://www.loc.gov/item/2016397707)
+- [Gulistān](https://www.loc.gov/item/2017406684/)
+- [Qajar-era poetry](https://www.loc.gov/item/2017498320/)
+
+The images were pre-processed using the following steps:
+
+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.
+
+### Annotations
+
+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).
+
+#### Annotators:
+- [Hajebrahimi Alireza](https://www.linkedin.com/in/alireza-hajebrahimi/)
+- [Hajebrahimi Reyhaneh](https://www.linkedin.com/in/reyhaneh-hajebrahimi-2565451a0/)
+
+
+### Citation Information
+
+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
+
+**BibTeX:**
+
+```bibtex
+@InProceedings{10.1007/978-3-031-53969-5_20,
+    author="Hajebrahimi, Alireza
+    and Santoso, Michael Evan
+    and Kovacs, Mate
+    and Kryssanov, Victor V.",
+    editor="Nicosia, Giuseppe
+    and Ojha, Varun
+    and La Malfa, Emanuele
+    and La Malfa, Gabriele
+    and Pardalos, Panos M.
+    and Umeton, Renato",
+    title="Few-Shot Learning for Character Recognition in Persian Historical Documents",
+    booktitle="Machine Learning, Optimization, and Data Science",
+    year="2024",
+    publisher="Springer Nature Switzerland",
+    address="Cham",
+    pages="259--273",
+    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.",
+    isbn="978-3-031-53969-5"
+}
+```