Historical handwritten dataset & benchmark results for Ethiopic script recognition
# HHD-Ethiopic
A text-line level historical handwritten Ethiopic OCR Dataset
## Overview
This repository contains a historical handwritten dataset called HHD-Ethiopic, and baselines models and human-level performance for benchmarking Historical Handwritten Ethiopic text-image recognition. HHD-Ethiopic is a text-line level historical handwritten Ethiopic OCR Dataset specifically designed for historical handwritten Ethiopic text-image recognition tasks. The full paper is here.
## Dataset Details
The HHD-Ethiopic OCR dataset consists of ~80k text-line images extracted from $18^{th}$ to $20^{th}$ centuries of historical handwritten Ethiopic manuscripts. Each text-line image is accompanied by its ground-truth text line transcription. The dataset can be directly downloaded from Hugging Face HHD-Ethiopic Dataset and/or Zenodo HHD-Ethiopic Dataset. Additional synthetically generated Ethiopic text-line images and their corresponding ground truth texts are available from this link.
Sample text-line images and their corresponding ground-truth text are shown below. For a more thorough tutorial about the dataset see formats of the dataset
| No. | Text-line Image | Ground-Truth Text |
|--|-------|------------------|
| [Image 1] | | ወጽራኅየኒ፡ቦአ፡ቅድሜሁ፡ውስተ፡ዕዘኒሁ |
| [Image 2] | | ፍራስ፡እሳት፡ወጽሩዓን |
| [Image 3] | | ወአንሰ፡በብዝኃ፡አሀውዕ፡ቢተኩ |
| [Image 4] | | ወአድኅነከ፡ይትፌሥሑ። |
## Getting Started
In the current implementation, the NumPy format of the HHD-Ethiopic dataset is used for training and testing the baseline models. Download the dataset.
After downloading HHD-Ethiopic, install the requirements, to demonstrate we just used the Train data and Test data stored in numpy format. To train and test all baseline models, please use all source codes link.
```markdown
pip install -r requirements.txt
```
To Train the model from scratch
```markdown
$ python3 train_model_plain_CTC.py
```
Alternatively, you can also run the training code demonstration in Google Colab directly .
To Pre …