task_categories:feature-extraction size_categories:10K<n<100K license:mit ocr numbers computervision region:us
---
license: mit
task_categories:
- feature-extraction
tags:
- ocr
- numbers
- computervision
size_categories:
- 10K<n<100K
---
# Dataset Card for PypayaNumbers

## Dataset Description

### Dataset Summary

This dataset consists of images of numbers along with their bounding box coordinates and labels. The dataset is divided into train and test sets, with each set containing images, numbers, and bounding boxes. The numbers are represented as one-line text files, while the bounding boxes are in YOLO format.

### Supported Tasks and Leaderboards

This dataset supports the task of Optical Character Recognition (OCR) and object detection. Specifically, it can be used for tasks like digit recognition in images.

### Languages

The dataset does not contain any natural language data.

## Dataset Structure

### Data Instances

Each instance in the dataset comprises an image file, a corresponding text file with the number represented in the image, and a text file with the bounding box coordinates for each digit in YOLO format.

### Data Fields

- `image`: A file path to an image containing a number.
- `number`: A file path to a text file containing the number represented in the image.
- `bounding_boxes`: A file path to a text file containing the bounding box coordinates for each digit in the image.

### Data Splits

The dataset is split into a training set of 5000 instances and a testing set of 2500 instances.

## Dataset Creation

### Curation Rationale

This dataset was curated to support the development and evaluation of models for digit recognition in images.

### Source Data

#### Initial Data Collection and Normalization

The images and labels in this dataset were generated by taking screenshots from various computer games and programs and cutting fragments containing numbers.

#### Who are the source language producers?

N/A

### Annotations

#### Annotation process

The bounding box annotations were generated using open-source LabelImg software.

#### Who are the annotators?

PypayaTech

### Personal and Sensitive Information

The dataset does not contain any personal or sensitive information.

## Considerations for Using the Data

### Social Impact of Dataset

This dataset could help in improving models for digit recognition in images, which has numerous applications including automated data entry, number plate recognition, and form digitization.

### Discussion of Biases

As the dataset consists of synthetic images of numbers, no inherent biases related to human demographics or behavior are expected.

### Other Known Limitations

The dataset only contains images of numbers and might not generalize well to other types of characters or more complex images.

## Additional Information

### Dataset Curators

PypayaTech

### Licensing Information

This dataset is licensed under the MIT license.

### Contributions

Contributions to the dataset are welcome. Please contact the dataset curator for more information.