task_categories:image-to-text size_categories:1K<n<10K license:cc-by-4.0 region:us
---
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
dataset_info:
  features:
  - name: image
    dtype: image
  - name: image_id
    dtype: int64
  - name: width
    dtype: int64
  - name: height
    dtype: int64
  - name: bbox
    sequence:
      sequence: float64
  - name: target
    sequence: string
  splits:
  - name: train
    num_bytes: 158666312.832
    num_examples: 6176
  - name: validation
    num_bytes: 48023349.6
    num_examples: 1765
  - name: test
    num_bytes: 22606532
    num_examples: 882
  download_size: 236835357
  dataset_size: 229296194.43199998
license: cc-by-4.0
task_categories:
- image-to-text
size_categories:
- 1K<n<10K
---
# Dataset Card for "license-plate-text-recognition-full"

## Background Information

This dataset is generated from `keremberke/license-plate-object-detection` dataset. What we have done is:

- Get the Bounding Boxes for each plate in an image,
- Crop the image to make the plate only visible,
- Run it through the `microsoft/trocr-large-printed` model to extract the written information.

## Structure of the Dataset

It has the same structure as the `keremberke/license-plate-object-detection` dataset, whereas we have added `target` column for each identified plate in an image.

## How to use it?

1. Install [datasets](https://pypi.org/project/datasets/)

```
pip install datasets
```

2. Load the dataset:

```
import datasets

ds = datasets.load_dataset("sonnetechnology/license-plate-text-recognition-full")
example = ds['train'][0]
```