GNHK: A Dataset for English Handwriting in the Wild

Alex W. C. Lee1*, Jonathan Chung2* and Marco Lee1
1 GoodNotes, Hong Kong - {alex, marco} [at] goodnotes (dot) com
2 Amazon Web Services, Vancouver - jonchung [at] amazon (dot) com
* denotes equal contribution
ICDAR 2021 - arXiv / PDF / GitHub
Examples of the handwriting images captured under unconstrained settings


In this paper, we present the GoodNotes Handwriting Kollection (GNHK) dataset. The GNHK dataset includes unconstrained camera-captured images of English handwritten text sourced from different regions around the world. The dataset is modeled after scene text datasets allowing researchers to investigate new localisation and text recognition techniques. We presented benchmark text localisation and recognition results with well-studied frameworks.


If you have any questions about the paper, please contact the primary authors: Alex Lee and Jonathan Chung.


If you find this dataset useful in your research, we would appreciate you citing this paper:

   author={Lee, Alex W. C. and Chung, Jonathan and Lee, Marco},
   booktitle={International Conference of Document Analysis and Recognition (ICDAR)},
   title={GNHK: A Dataset for English Handwriting in the Wild},

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The GNHK dataset is free to download under a CC-BY-4.0 License.

The dataset is provided “as is”, without warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall the authors and the legal entities they represent be liable for any claim, damages or other liability whatsoever, whether in an action of contract, tort or otherwise, arising from, out of or in connection with the dataset or the use or other dealings in the dataset.

If any of the documents contain your information and you do not want them in this dataset, please contact the authors of the paper and we will remove the data expeditiously.

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URLs on Google Drive
SageMaker JSON format (train & test)Paper JSON format (train & test)
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Paper JSON format (train) [pw: dr6b]Paper JSON format (test) [pw: 6h6s]
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