## HHD-Ethiopic Dataset
This dataset, named "HHD-Ethiopic," is designed for ethiopic text-image recognition tasks. It contains a collection of historical handwritten Manuscripts in the Ethiopic script. The dataset is intended to facilitate research and development for Ethiopic text-image recognition.

### Dataset Details/
- __Size__: 79,684 <br>
- __Training Set__: 57,374 <br>
- __Test Set__: HHD-Ethiopic consists of two separate Test sets
  - __Test Set I (IID)__: 6,375 images (randomly drawn from the training set)
  - __Test Set II (OOD)__: 15,935 images (specifically from manuscripts dated in the 18th century) <br>
- __Validation Set__: 10% of the training set, randomly drawn <br>
- __Number of unique Ethiopic characters__ :306
- __Dataset Formats__:the HHD-Ethiopic dataset is stored in two different formats to accommodate different use cases:
  - __Raw Image and Ground-truth Text__: consistes of the original images and their corresponding ground-truth text.
    The dataset is structured as raw images (.png) accompanied by a [train CSV file](https://huggingface.co/datasets/OCR-Ethiopic/HHD-Ethiopic/blob/main/train/train_raw/image_text_pairs_train.csv), [test-I CSV file](https://huggingface.co/datasets/OCR-Ethiopic/HHD-Ethiopic/blob/main/test/test_rand/image_text_pairs_test_rand.csv), and [test-II CSV file](https://huggingface.co/datasets/OCR-Ethiopic/HHD-Ethiopic/blob/main/test/test_18th/image_text_pairs_test_18th.csv) that contains the file names of the images and their respective ground-truth text for the training and two test sets respectively.<br>
  -__Numpy Format__: in this format, both the images and the ground-truth text are stored in a convenient numpy format. The dataset provides pre-processed numpy arrays that can be directly used for training and testing models.


- __Metadata__(Human Level Performance ): we have also included metadata regarding the human-level performance predicted by individuals for the test sets. This metadata provides insights into the expected performance-level that humans can achieve in historical Ethiopic text-image recognition tasks.
  - __Test Set I__  - for test set I, a group of 9 individuals was presented with a random subset of the dataset. They were asked to perform Ethiopic text-image recognition and provide their best efforts to transcribe the handwritten texts. The results were collected and stored in a CSV file, [Test-I-human_performance](https://github.com/bdu-birhanu/HHD-Ethiopic/blob/main/Dataset/human-level-predictions/6375_new_all.csv) included in the dataset.
  - __Test Set II__ - Test set II which was prepared exclusively from Ethiopic historical handwritten documents dated in the 18th century. A different group  of 4 individuals was given this subset for evaluation.  The human-level performance predictions for this set are also stored in a separate CSV file, [Test-II_human_performance](https://github.com/bdu-birhanu/HHD-Ethiopic/blob/main/Dataset/human-level-predictions/15935_new_all.csv)
 Please refer to the respective CSV files for detailed information on the human-level performance predictions. Each CSV file contains the necessary metadata, including the image filenames, groind-truth and the corresponding human-generated transcriptions.
If you would like to explore or analyze the human-level performance data further, please refer to the provided CSV files.




#### Citation
If you use the hhd-ethiopic dataset in your research, please consider citing it:

```
@misc {author_2023,
  author = { {Anonymous-author},
	title  = { HHD-Ethiopic:A Historical Handwritten Dataset for Ethiopic OCR with Baseline Models and Human-level Performance (Revision 50c1e04) },
	year   = 2023,
	url    = { https://huggingface.co/datasets/OCR-Ethiopic/HHD-Ethiopic },
	doi    = { 10.57967/hf/0691 },
	publisher = { Hugging Face }
}
```

#### License
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.

license:cc-by-4.0 doi:10.57967/hf/0691 region:us
{"license": "cc-by-4.0"}
TAGS
#license-cc-by-4.0 #doi-10.57967/hf/0691 #region-us 

## HHD-Ethiopic Dataset
This dataset, named "HHD-Ethiopic," is designed for ethiopic text-image recognition tasks. It contains a collection of historical handwritten Manuscripts in the Ethiopic script. The dataset is intended to facilitate research and development for Ethiopic text-image recognition.

### Dataset Details/
- __Size__: 79,684 <br>
- __Training Set__: 57,374 <br>
- __Test Set__: HHD-Ethiopic consists of two separate Test sets
  - __Test Set I (IID)__: 6,375 images (randomly drawn from the training set)
  - __Test Set II (OOD)__: 15,935 images (specifically from manuscripts dated in the 18th century) <br>
- __Validation Set__: 10% of the training set, randomly drawn <br>
- __Number of unique Ethiopic characters__ :306
- __Dataset Formats__:the HHD-Ethiopic dataset is stored in two different formats to accommodate different use cases:
  - __Raw Image and Ground-truth Text__: consistes of the original images and their corresponding ground-truth text.
    The dataset is structured as raw images (.png) accompanied by a train CSV file, test-I CSV file, and test-II CSV file that contains the file names of the images and their respective ground-truth text for the training and two test sets respectively.<br>
  -__Numpy Format__: in this format, both the images and the ground-truth text are stored in a convenient numpy format. The dataset provides pre-processed numpy arrays that can be directly used for training and testing models.


- __Metadata__(Human Level Performance ): we have also included metadata regarding the human-level performance predicted by individuals for the test sets. This metadata provides insights into the expected performance-level that humans can achieve in historical Ethiopic text-image recognition tasks.
  - __Test Set I__  - for test set I, a group of 9 individuals was presented with a random subset of the dataset. They were asked to perform Ethiopic text-image recognition and provide their best efforts to transcribe the handwritten texts. The results were collected and stored in a CSV file, Test-I-human_performance included in the dataset.
  - __Test Set II__ - Test set II which was prepared exclusively from Ethiopic historical handwritten documents dated in the 18th century. A different group  of 4 individuals was given this subset for evaluation.  The human-level performance predictions for this set are also stored in a separate CSV file, Test-II_human_performance
 Please refer to the respective CSV files for detailed information on the human-level performance predictions. Each CSV file contains the necessary metadata, including the image filenames, groind-truth and the corresponding human-generated transcriptions.
If you would like to explore or analyze the human-level performance data further, please refer to the provided CSV files.




If you use the hhd-ethiopic dataset in your research, please consider citing it:



#### License
<a rel="license" href="URL alt="Creative Commons License" style="border-width:0" src="https://i.URL /></a><br />This work is licensed under a <a rel="license" href="URL Commons Attribution 4.0 International License</a>.

## HHD-Ethiopic Dataset
This dataset, named "HHD-Ethiopic," is designed for ethiopic text-image recognition tasks. It contains a collection of historical handwritten Manuscripts in the Ethiopic script. The dataset is intended to facilitate research and development for Ethiopic text-image recognition. ### Dataset Details/
- __Size__: 79,684 <br>
- __Training Set__: 57,374 <br>
- __Test Set__: HHD-Ethiopic consists of two separate Test sets
  - __Test Set I (IID)__: 6,375 images (randomly drawn from the training set)
  - __Test Set II (OOD)__: 15,935 images (specifically from manuscripts dated in the 18th century) <br>
- __Validation Set__: 10% of the training set, randomly drawn <br>
- __Number of unique Ethiopic characters__ :306
- __Dataset Formats__:the HHD-Ethiopic dataset is stored in two different formats to accommodate different use cases:
  - __Raw Image and Ground-truth Text__: consistes of the original images and their corresponding ground-truth text.
    The dataset is structured as raw images (.png) accompanied by a train CSV file, test-I CSV file, and test-II CSV file that contains the file names of the images and their respective ground-truth text for the training and two test sets respectively.<br>
  -__Numpy Format__: in this format, both the images and the ground-truth text are stored in a convenient numpy format. The dataset provides pre-processed numpy arrays that can be directly used for training and testing models.


- __Metadata__(Human Level Performance ): we have also included metadata regarding the human-level performance predicted by individuals for the test sets. This metadata provides insights into the expected performance-level that humans can achieve in historical Ethiopic text-image recognition tasks.
  - __Test Set I__  - for test set I, a group of 9 individuals was presented with a random subset of the dataset. They were asked to perform Ethiopic text-image recognition and provide their best efforts to transcribe the handwritten texts. The results were collected and stored in a CSV file, Test-I-human_performance included in the dataset.
  - __Test Set II__ - Test set II which was prepared exclusively from Ethiopic historical handwritten documents dated in the 18th century. A different group  of 4 individuals was given this subset for evaluation.  The human-level performance predictions for this set are also stored in a separate CSV file, Test-II_human_performance
 Please refer to the respective CSV files for detailed information on the human-level performance predictions. Each CSV file contains the necessary metadata, including the image filenames, groind-truth and the corresponding human-generated transcriptions.
If you would like to explore or analyze the human-level performance data further, please refer to the provided CSV files.




If you use the hhd-ethiopic dataset in your research, please consider citing it: #### License
<a rel="license" href="URL alt="Creative Commons License" style="border-width:0" src="https://i.URL /></a><br />This work is licensed under a <a rel="license" href="URL Commons Attribution 4.0 International License</a>.
TAGS
#license-cc-by-4.0 #doi-10.57967/hf/0691 #region-us 
 ## HHD-Ethiopic Dataset
This dataset, named "HHD-Ethiopic," is designed for ethiopic text-image recognition tasks. It contains a collection of historical handwritten Manuscripts in the Ethiopic script. The dataset is intended to facilitate research and development for Ethiopic text-image recognition. ### Dataset Details/
- __Size__: 79,684 <br>
- __Training Set__: 57,374 <br>
- __Test Set__: HHD-Ethiopic consists of two separate Test sets
  - __Test Set I (IID)__: 6,375 images (randomly drawn from the training set)
  - __Test Set II (OOD)__: 15,935 images (specifically from manuscripts dated in the 18th century) <br>
- __Validation Set__: 10% of the training set, randomly drawn <br>
- __Number of unique Ethiopic characters__ :306
- __Dataset Formats__:the HHD-Ethiopic dataset is stored in two different formats to accommodate different use cases:
  - __Raw Image and Ground-truth Text__: consistes of the original images and their corresponding ground-truth text.
    The dataset is structured as raw images (.png) accompanied by a train CSV file, test-I CSV file, and test-II CSV file that contains the file names of the images and their respective ground-truth text for the training and two test sets respectively.<br>
  -__Numpy Format__: in this format, both the images and the ground-truth text are stored in a convenient numpy format. The dataset provides pre-processed numpy arrays that can be directly used for training and testing models.


- __Metadata__(Human Level Performance ): we have also included metadata regarding the human-level performance predicted by individuals for the test sets. This metadata provides insights into the expected performance-level that humans can achieve in historical Ethiopic text-image recognition tasks.
  - __Test Set I__  - for test set I, a group of 9 individuals was presented with a random subset of the dataset. They were asked to perform Ethiopic text-image recognition and provide their best efforts to transcribe the handwritten texts. The results were collected and stored in a CSV file, Test-I-human_performance included in the dataset.
  - __Test Set II__ - Test set II which was prepared exclusively from Ethiopic historical handwritten documents dated in the 18th century. A different group  of 4 individuals was given this subset for evaluation.  The human-level performance predictions for this set are also stored in a separate CSV file, Test-II_human_performance
 Please refer to the respective CSV files for detailed information on the human-level performance predictions. Each CSV file contains the necessary metadata, including the image filenames, groind-truth and the corresponding human-generated transcriptions.
If you would like to explore or analyze the human-level performance data further, please refer to the provided CSV files.




If you use the hhd-ethiopic dataset in your research, please consider citing it: #### License
<a rel="license" href="URL alt="Creative Commons License" style="border-width:0" src="https://i.URL /></a><br />This work is licensed under a <a rel="license" href="URL Commons Attribution 4.0 International License</a>.
passage: TAGS
#license-cc-by-4.0 #doi-10.57967/hf/0691 #region-us 
## HHD-Ethiopic Dataset
This dataset, named "HHD-Ethiopic," is designed for ethiopic text-image recognition tasks. It contains a collection of historical handwritten Manuscripts in the Ethiopic script. The dataset is intended to facilitate research and development for Ethiopic text-image recognition.