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deepcopy/handwritten-text-recognition-bongabdo

Dataset Card for Bongabdo Dataset Summary Bongabdo is a curated dataset of full-page Bangla (Bengali) handwritten text, intended for use in offline handwriting recognition tasks using modern neural architectures. It includes high-resolution scanned images of handwritten Bangla scripts, transcriptions, and rich per-document metadata. The data has been contributed by people of diverse age groups, occupations, and genders, making it well-suited for training robust… See the full description on the dataset page: https://huggingface.co/datasets/deepcopy/handwritten-text-recognition-bongabdo.

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Dataset Card

Dataset Card for Bongabdo

Dataset Summary

Bongabdo is a curated dataset of full-page Bangla (Bengali) handwritten text, intended for use in offline handwriting recognition tasks using modern neural architectures. It includes high-resolution scanned images of handwritten Bangla scripts, transcriptions, and rich per-document metadata. The data has been contributed by people of diverse age groups, occupations, and genders, making it well-suited for training robust handwriting recognition models that generalize well across handwriting styles.

The dataset is especially valuable given the low-resource nature of the Bengali language in handwriting datasets. It was introduced in the research paper:

Towards Full-page Offline Bangla Handwritten Text Recognition using Image-to-Sequence Architecture Ayanabha Ghosh, 2023 – IEEE Silchar Subsection Conference, Silchar, Assam, India.

This dataset was ported from https://www.kaggle.com/datasets/joebeachcapital/handwritten-text-recognition-bongabdo

Supported Tasks

—Offline Handwritten Text Recognition (HTR)
—Document Layout Understanding (potentially, using zone-level metadata)
—Bangla NLP Preprocessing (from human handwriting)

Dataset Structure

Each example in the dataset contains the following:

Features

Column Name	Type	Description
`image`	Image	Full-page scanned handwritten Bangla document.
`text`	string	Transcription of the handwritten text from the document (Unicode Bangla).
`SN`	int64	Serial number of the example.
`Filename`	string	Name of the image and annotation files.
`Username`	string	An anonymized contributor ID.
`Age`	int64	Age of the writer.
`Gender`	string	Gender of the writer (M/F).
`Occupation`	string	Writer’s occupation (e.g., Student).
`Category`	string	Topical category of the text (e.g., News - Travel).
`Char Count`	float64	Number of characters in the transcription.
`Article link`	string	Original source URL (for typed text, not the handwriting).
`Strike`	bool	Whether the handwriting includes strike-throughs.
`Bangla - English`	bool	Whether the text includes code-switching between Bangla and English.
`Multi - Paragraph`	bool	Whether the document contains multiple paragraphs.

Data Splits

The current release does not include predefined training/validation/test splits. Users are encouraged to split the dataset as needed using train_test_split() from the datasets library or other strategies.

Usage Example

python

from datasets import load_dataset

ds = load_dataset("deepcopy/handwritten-text-recognition-bongabdo")
sample = ds["train"][0]
sample["image"].show()
print(sample["text"])

Citation

If you use this dataset in your research or development, please cite the following paper:

bibtex

@inproceedings{ghosh2023bangla,
  title={Towards Full-page Offline Bangla Handwritten Text Recognition using Image-to-Sequence Architecture},
  author={Ghosh, Ayanabha},
  booktitle={IEEE Silchar Subsection Conference},
  year={2023},
  address={Silchar, Assam, India}
}

Licensing

Attribution 4.0 International (CC BY 4.0)