| | --- |
| | dataset_info: |
| | features: |
| | - name: filename |
| | dtype: string |
| | - name: image |
| | dtype: image |
| | - name: text |
| | dtype: string |
| | splits: |
| | - name: train |
| | num_bytes: 164401783.625 |
| | num_examples: 2043 |
| | download_size: 161098467 |
| | dataset_size: 164401783.625 |
| | configs: |
| | - config_name: default |
| | data_files: |
| | - split: train |
| | path: data/train-* |
| | license: mit |
| | task_categories: |
| | - translation |
| | language: |
| | - mr |
| | tags: |
| | - history, |
| | size_categories: |
| | - 1K<n<10K |
| | --- |
| | |
| |
|
| | # MoDeTrans: Modi to Devanagari Transliteration Dataset |
| |
|
| | This dataset supports research in the transliteration of **Modi script**—an ancient Indian script historically used in Maharashtra—into **Devanagari**, the script currently used for Marathi and several other Indian languages. |
| |
|
| | The dataset was introduced in the following paper: |
| |
|
| | > **Historic Scripts to Modern Vision: A Novel Dataset and A VLM Framework for Transliteration of Modi Script to Devanagari** |
| | > *Harshal Kausadikar, Tanvi Kale, Onkar Susladkar, Sparsh Mittal* |
| | > **Accepted at ICDAR 2025** |
| | > [Paper Link](https://doi.org/10.1007/978-3-032-04630-7_3) |
| |
|
| | ## Dataset Features |
| |
|
| | - **Modi script image patches** |
| | - **Aligned Devanagari transliterations** |
| | - Covers a diverse range of historical Modi handwriting styles |
| | - Suitable for OCR, script transliteration, vision-language modeling, and low-resource script research |
| |
|
| | ## Usage |
| |
|
| | This dataset is ideal for: |
| | - Handwritten script recognition and transliteration |
| | - Vision-language model (VLM) training for historical scripts |
| | - Document digitization and cultural heritage preservation |
| |
|
| | ## SynthMoDe Dataset |
| |
|
| | The synthetic version of above MoDeTrans (**Mo**di to **De**vanagari **Trans**literation) Dataset is available at: https://huggingface.co/datasets/historyHulk/SynthMoDe |
| |
|
| |
|
| | ## Citation |
| |
|
| | If you use this dataset in your research or publications, **please cite the following paper**: |
| |
|
| | ```bibtex |
| | @InProceedings{10.1007/978-3-032-04630-7_3, |
| | author="Kausadikar, Harshal |
| | and Kale, Tanvi |
| | and Susladkar, Onkar |
| | and Mittal, Sparsh", |
| | editor="Yin, Xu-Cheng |
| | and Karatzas, Dimosthenis |
| | and Lopresti, Daniel", |
| | title="Historic Scripts to Modern Vision: A Novel Dataset and A VLM Framework for Transliteration of Modi Script to Devanagari", |
| | booktitle="Document Analysis and Recognition -- ICDAR 2025", |
| | year="2026", |
| | publisher="Springer Nature Switzerland", |
| | address="Cham", |
| | pages="40--61", |
| | abstract="In medieval India, the Marathi language was written using the Modi script. The texts written in Modi script include extensive knowledge about medieval sciences, medicines, land records and authentic evidence about Indian history. Around 40 million documents are in poor condition and have not yet been transliterated. Furthermore, only a few experts in this domain can transliterate this script into English or Devanagari. Most of the past research predominantly focuses on individual character recognition. A system that can transliterate Modi script documents to Devanagari script is needed. We propose the MoDeTrans dataset, comprising 2,043 images of Modi script documents accompanied by their corresponding textual transliterations in Devanagari. We further introduce MoScNet (Modi Script Network), a novel Vision-Language Model (VLM) framework for transliterating Modi script images into Devanagari text. MoScNet leverages Knowledge Distillation, where a student model learns from a teacher model to enhance transliteration performance. The final student model of MoScNet has better performance than the teacher model while having 163{\$}{\$}{\backslash}times {\$}{\$}{\texttimes}lower parameters. Our work is the first to perform direct transliteration from the handwritten Modi script to the Devanagari script. MoScNet also shows competitive results on the optical character recognition (OCR) task.", |
| | isbn="978-3-032-04630-7" |
| | } |
| | ``` |
