Datasets:
Tasks:
Image Classification
Modalities:
Image
Formats:
imagefolder
Languages:
English
Size:
100K - 1M
Tags:
OCR
Handwriting
Character Recognition
Grayscale Images
ASCII Labels
Optical Character Recognition
License:
Louis Rädisch
commited on
Commit
·
f0a06da
1
Parent(s):
c1c308b
Update README.md
Browse files
README.md
CHANGED
|
@@ -1,3 +1,58 @@
|
|
| 1 |
---
|
| 2 |
license: mit
|
| 3 |
---
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
---
|
| 2 |
license: mit
|
| 3 |
---
|
| 4 |
+
# AlphaNum Dataset
|
| 5 |
+
|
| 6 |
+
## Table of Contents
|
| 7 |
+
- [Dataset Summary](#dataset-summary)
|
| 8 |
+
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
|
| 9 |
+
- [Languages](#languages)
|
| 10 |
+
- [Dataset Structure](#dataset-structure)
|
| 11 |
+
- [Data Instances](#data-instances)
|
| 12 |
+
- [Data Fields](#data-fields)
|
| 13 |
+
- [Data Splits](#data-splits)
|
| 14 |
+
- [Dataset Creation](#dataset-creation)
|
| 15 |
+
- [Curation Rationale](#curation-rationale)
|
| 16 |
+
- [Source Data](#source-data)
|
| 17 |
+
- [Dataset Use](#dataset-use)
|
| 18 |
+
- [Use Cases](#use-cases)
|
| 19 |
+
- [Usage Caveats](#usage-caveats)
|
| 20 |
+
- [Getting Started](#getting-started)
|
| 21 |
+
|
| 22 |
+
## Dataset Summary
|
| 23 |
+
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.
|
| 24 |
+
|
| 25 |
+
## Supported Tasks and Leaderboards
|
| 26 |
+
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.
|
| 27 |
+
|
| 28 |
+
## Languages
|
| 29 |
+
The dataset is primarily in English.
|
| 30 |
+
|
| 31 |
+
## Dataset Structure
|
| 32 |
+
### Data Instances
|
| 33 |
+
A data instance in this dataset represents an image of a handwritten character and the associated labels.
|
| 34 |
+
|
| 35 |
+
### Data Fields
|
| 36 |
+
The fields are:
|
| 37 |
+
1) 'image': The image of the handwritten character.
|
| 38 |
+
2) 'label': The label for the character in the image.
|
| 39 |
+
|
| 40 |
+
### Data Splits
|
| 41 |
+
The dataset is split into training, validation, and test sets.
|
| 42 |
+
|
| 43 |
+
## Dataset Creation
|
| 44 |
+
### Curation Rationale
|
| 45 |
+
The dataset was created to provide a rich and diverse source of data for the development and improvement of OCR models.
|
| 46 |
+
|
| 47 |
+
### Source Data
|
| 48 |
+
The source data comes from handwritten characters created by various individuals.
|
| 49 |
+
|
| 50 |
+
## Dataset Use
|
| 51 |
+
### Use Cases
|
| 52 |
+
The dataset can be used for tasks related to text recognition, document processing, and machine learning.
|
| 53 |
+
|
| 54 |
+
### Usage Caveats
|
| 55 |
+
There are no specific restrictions on the use of this dataset.
|
| 56 |
+
|
| 57 |
+
### Getting Started
|
| 58 |
+
The dataset can be utilized for the development and improvement of OCR models.
|