Datasets:
Tasks:
Image Classification
Modalities:
Image
Formats:
imagefolder
Languages:
English
Size:
100K<n<1M
Tags:
OCR
Handwriting
Character Recognition
Grayscale Images
ASCII Labels
Optical Character Recognition
License:
Louis Rädisch
commited on
Commit
•
f0a06da
1
Parent(s):
c1c308b
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README.md
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license: mit
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---
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license: mit
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---
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# AlphaNum Dataset
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## Table of Contents
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- [Dataset Summary](#dataset-summary)
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
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- [Languages](#languages)
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- [Dataset Structure](#dataset-structure)
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- [Data Instances](#data-instances)
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- [Data Fields](#data-fields)
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- [Data Splits](#data-splits)
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- [Dataset Creation](#dataset-creation)
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- [Curation Rationale](#curation-rationale)
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- [Source Data](#source-data)
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- [Dataset Use](#dataset-use)
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- [Use Cases](#use-cases)
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- [Usage Caveats](#usage-caveats)
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- [Getting Started](#getting-started)
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## Dataset Summary
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The AlphaNum dataset, created by Louis Rädisch, is a comprehensive handwritten dataset specifically designed for the development and improvement of OCR (Optical Character Recognition) models. The dataset comprises handwritten characters A-Z and numbers 0-9, providing a realistic challenge for OCR models.
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## Supported Tasks and Leaderboards
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This dataset supports a range of tasks including text recognition, document processing, and machine learning. It can contribute to improving the performance of OCR models by providing a diverse set of writing styles and formats.
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## Languages
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The dataset is primarily in English.
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## Dataset Structure
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### Data Instances
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A data instance in this dataset represents an image of a handwritten character and the associated labels.
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### Data Fields
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The fields are:
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1) 'image': The image of the handwritten character.
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2) 'label': The label for the character in the image.
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### Data Splits
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The dataset is split into training, validation, and test sets.
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## Dataset Creation
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### Curation Rationale
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The dataset was created to provide a rich and diverse source of data for the development and improvement of OCR models.
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### Source Data
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The source data comes from handwritten characters created by various individuals.
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## Dataset Use
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### Use Cases
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The dataset can be used for tasks related to text recognition, document processing, and machine learning.
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### Usage Caveats
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There are no specific restrictions on the use of this dataset.
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### Getting Started
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The dataset can be utilized for the development and improvement of OCR models.
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