---
annotations_creators:
- manual
language:
- pt
- en
license: mit
multilinguality: monolingual
pretty_name: Portuguese OCR Dataset
size_categories:
- 1K<n<10K
source_datasets: []
task_categories:
- image-to-text
tags:
- ocr
- Portuguese
---

# 📘 Portuguese OCR Dataset

This dataset contains scanned image-text pairs in **European Portuguese**, curated manually for Optical Character Recognition (OCR) tasks. It includes literary sentences and historical excerpts.

## 📦 Dataset Overview

- **Total Samples**: 10,000
- **Image Shape**: `(10000, 100, 1200, 3)` — color images with height 100 and width 1200 pixels
- **Text Example**:  
  `"E mais avante o Estreito que se arreia"`

## 📂 Dataset Structure

The dataset is stored in a **single HDF5 file** (`dataset.h5`), which includes:

- `images`: a NumPy array of grayscale image data (from PNG files)
- `texts`: a list of UTF-8 encoded strings, corresponding to each image

Each sample pairs an image and its corresponding transcription.

## 📊 Dataset Statistics

- Format: HDF5 (`dataset.h5`)
- Total samples: ~10,000
- Average image size: 224x224 pixels (if preprocessed)
- Language: European Portuguese
- Source: literary and historic texts

## 💾 How to Load

To use this dataset with the Hugging Face Datasets library:

```python
from datasets import load_dataset
import h5py

# Load from Hugging Face
dataset_path = "mazafard/portugues_ocr_dataset"
h5_file = load_dataset(dataset_path, data_files="dataset.h5", split="train")

# Alternatively, open the HDF5 file directly
with h5py.File("dataset.h5", "r") as f:
    images = f["images"][:]        # NumPy array of images
    texts = f["texts"][:]          # List of transcriptions
```

## 🧠 Use Cases

- Fine-tuning OCR models like [`microsoft/trocr-base-printed`](https://huggingface.co/microsoft/trocr-base-printed)
- Document digitization
- Research in Portuguese language modeling and handwritten/printed recognition

## 📜 License

MIT License — free for academic and commercial use with attribution.



## 📦 How to Inspect `dataset.h5`

You can extract metadata from the HDF5 file using `h5py`:

```python
import h5py

with h5py.File("dataset.h5", "r") as f:
    print("Keys:", list(f.keys()))  # ['images', 'texts']
    print("Number of samples:", len(f["texts"]))
    print("Image shape:", f["images"].shape)
    print("Example text:", f["texts"][0])
```

### ⚠️ Note

This dataset uses synthetic text-image pairs for printed OCR. Handwritten or scanned documents are not included.