
# AlphaNum Dataset

![AlphaNum](assets/1.png)


## Abstract
The AlphaNum dataset is a collection of 108.791 grayscale images of handwritten characters and numerals as well as special character, each sized 24x24 pixels. This dataset is designed to bolster Optical Character Recognition (OCR) research and development.

For consistency, images extracted from the MNIST dataset have been color-inverted to match the grayscale aesthetics of the AlphaNum dataset.

## Data Sources
1) [Handwriting Characters Database](https://github.com/sueiras/handwritting_characters_database)
2) [MNIST](https://huggingface.co/datasets/mnist)
3) [AZ Handwritten Alphabets in CSV format](https://www.kaggle.com/datasets/sachinpatel21/az-handwritten-alphabets-in-csv-format)

In an effort to maintain uniformity, the dataset files have been resized to 24x24 pixels and recolored from white-on-black to black-on-white.

## Dataset Structure
### Instance Description
Each dataset instance contains an image of a handwritten character or numeral, paired with its corresponding ASCII label.

### Data Organization
The dataset is organized into three separate .zip files: `train.zip`, `test.zip`, and `validation.zip`. Each ASCII symbol is housed in a dedicated folder, the name of which corresponds to the ASCII value of the symbol.
- `train.zip` size: 55.9 MB
- `test.zip` size: 16 MB
- `validation.zip` size: 8.06 MB

## Dataset Utility
The AlphaNum dataset caters to a variety of use cases including text recognition, document processing, and machine learning tasks. It is particularly instrumental in the development, fine-tuning, and enhancement of OCR models.

## Null Category Image Generation
The 'null' category comprises images generated by injecting noise to mimic randomly distributed light pixels. The creation of these images is accomplished through the following Python script:
This approach is particularly valuable as it enables the model to effectively disregard specific areas of the training data by utilizing a 'null' label. By doing so, the model becomes better at recognizing letters and can ignore irrelevant parts, enhancing its performance in reallive OCR tasks.

The 'null' labelled images in this dataset have been generated using the following algorithm. 
(Please note that this is a non-deterministic approach, so you will most likely get different results.)
```python
import os
import numpy as np
from PIL import Image, ImageOps, ImageEnhance

def generate_noisy_images(num_images, image_size=(24, 24) output_dir='NoisyImages', image_format='JPEG'):
    if not os.path.exists(output_dir):
        os.makedirs(output_dir)
        
    for i in range(num_images):
        variation_scale = abs(np.random.normal(30, 15))
        # Generate random noise with reduced strength
        noise = np.random.rand(image_size[0], image_size[1]) * 0.05
        noise = (noise * 255).astype(np.uint8)
        
        # Create a PIL image from the noise
        image = Image.fromarray(noise, mode='L')  # 'L' for grayscale
        
        # Invert the image
        inverted_image = ImageOps.invert(image)
        
        # Enhance the contrast with increased amplitude
        enhancer = ImageEnhance.Contrast(inverted_image)
        contrast_enhanced_image = enhancer.enhance(variation_scale)  # Increased amplitude (e.g., 3.0)
        
        # Save the image
        contrast_enhanced_image.save(os.path.join(output_dir, f'{i}.jpg'), format=image_format)

generate_noisy_images(5000)
```

example: ![noisy Image](assets/0.jpg)


## ASCII Table and Corresponding File Counts
| ASCII Value | Character | Number of Files |
|-------------|-----------|-----------------|
| 33          | !         | 207             |
| 34          | "         | 267             |
| 35          | #         | 152             |
| 36          | $         | 192             |
| 37          | %         | 190             |
| 38          | &         | 104             |
| 39          | '         | 276             |
| 40          | (         | 346             |
| 41          | )         | 359             |
| 42          | *         | 128             |
| 43          | +         | 146             |
| 44          | ,         | 320             |
| 45          | -         | 447             |
| 46          | .         | 486             |
| 47          | /         | 259             |
| 48          | 0         | 2664            |
| 49          | 1         | 2791            |
| 50          | 2         | 2564            |
| 51          | 3         | 2671            |
| 52          | 4         | 2530            |
| 53          | 5         | 2343            |
| 54          | 6         | 2503            |
| 55          | 7         | 2679            |
| 56          | 8         | 2544            |
| 57          | 9         | 2617            |
| 58          | :         | 287             |
| 59          | ;         | 223             |
| 60          | <         | 168             |
| 61          | =         | 254             |
| 62          | >         | 162             |
| 63          | ?         | 194             |
| 64          | @         | 83              |
| 65          | A         | 1923            |
| 66          | B         | 1505            |
| 67          | C         | 1644            |
| 68          | D         | 1553            |
| 69          | E         | 2171            |
| 70          | F         | 1468            |
| 71          | G         | 1443            |
| 72          | H         | 1543            |
| 73          | I         | 1888            |
| 74          | J         | 1470            |
| 75          | K         | 1504            |
| 76          | L         | 1692            |
| 77          | M         | 1484            |
| 78          | N         | 1683            |
| 79          | O         | 2097            |
| 80          | P         | 1605            |
| 81          | Q         | 1409            |
| 82          | R         | 1811            |
| 83          | S         | 1786            |
| 84          | T         | 1729            |
| 85          | U         | 1458            |
| 86          | V         | 1405            |
| 87          | W         | 1521            |
| 88          | X         | 1366            |
| 89          | Y         | 1456            |
| 90          | Z         | 1451            |
| 91          | [         | 111             |
| 93          | ]         | 104             |
| 94          | ^         | 88              |
| 95          | _         | 80              |
| 96          | `         | 42              |
| 97          | a         | 2219            |
| 98          | b         | 624             |
| 99          | c         | 880             |
| 100         | d         | 1074            |
| 101         | e         | 2962            |
| 102         | f         | 608             |
| 103         | g         | 760             |
| 104         | h         | 990             |
| 105         | i         | 2035            |
| 106         | j         | 427             |
| 107         | k         | 557             |
| 108         | l         | 1415            |
| 109         | m         | 879             |
| 110         | n         | 1906            |
| 111         | o         | 2048            |
| 112         | p         | 786             |
| 113         | q         | 427             |
| 114         | r         | 1708            |
| 115         | s         | 1557            |
| 116         | t         | 1781            |
| 117         | u         | 1319            |
| 118         | v         | 555             |
| 119         | w         | 680             |
| 120         | x         | 463             |
| 121         | y         | 680             |
| 122         | z         | 505             |
| 123         | {         | 73              |
| 124         | \|        | 91              |
| 125         | }         | 77              |
| 126         | ~         | 59              |
| 999         | null      | 4999            |
task_categories:image-classification size_categories:100K<n<1M language:en license:mit OCR Handwriting Character Recognition Grayscale Images ASCII Labels Optical Character Recognition region:us
{"language": ["en"], "license": "mit", "size_categories": ["100K<n<1M"], "task_categories": ["image-classification"], "pretty_name": "alphanum", "tags": ["OCR", "Handwriting", "Character Recognition", "Grayscale Images", "ASCII Labels", "Optical Character Recognition"]}
TAGS
#task_categories-image-classification #size_categories-100K<n<1M #language-English #license-mit #OCR #Handwriting #Character Recognition #Grayscale Images #ASCII Labels #Optical Character Recognition #region-us 

AlphaNum Dataset
================


!AlphaNum


Abstract
--------


The AlphaNum dataset is a collection of 108.791 grayscale images of handwritten characters and numerals as well as special character, each sized 24x24 pixels. This dataset is designed to bolster Optical Character Recognition (OCR) research and development.


For consistency, images extracted from the MNIST dataset have been color-inverted to match the grayscale aesthetics of the AlphaNum dataset.


Data Sources
------------


1. Handwriting Characters Database
2. MNIST
3. AZ Handwritten Alphabets in CSV format


In an effort to maintain uniformity, the dataset files have been resized to 24x24 pixels and recolored from white-on-black to black-on-white.


Dataset Structure
-----------------


### Instance Description


Each dataset instance contains an image of a handwritten character or numeral, paired with its corresponding ASCII label.


### Data Organization


The dataset is organized into three separate .zip files: 'URL', 'URL', and 'URL'. Each ASCII symbol is housed in a dedicated folder, the name of which corresponds to the ASCII value of the symbol.


* 'URL' size: 55.9 MB
* 'URL' size: 16 MB
* 'URL' size: 8.06 MB


Dataset Utility
---------------


The AlphaNum dataset caters to a variety of use cases including text recognition, document processing, and machine learning tasks. It is particularly instrumental in the development, fine-tuning, and enhancement of OCR models.


Null Category Image Generation
------------------------------


The 'null' category comprises images generated by injecting noise to mimic randomly distributed light pixels. The creation of these images is accomplished through the following Python script:
This approach is particularly valuable as it enables the model to effectively disregard specific areas of the training data by utilizing a 'null' label. By doing so, the model becomes better at recognizing letters and can ignore irrelevant parts, enhancing its performance in reallive OCR tasks.


The 'null' labelled images in this dataset have been generated using the following algorithm.
(Please note that this is a non-deterministic approach, so you will most likely get different results.)


example: !noisy Image


ASCII Table and Corresponding File Counts
-----------------------------------------


ASCII Value: 33, Character: !, Number of Files: 207
ASCII Value: 34, Character: ", Number of Files: 267
ASCII Value: 35, Character: #, Number of Files: 152
ASCII Value: 36, Character: $, Number of Files: 192
ASCII Value: 37, Character: %, Number of Files: 190
ASCII Value: 38, Character: &, Number of Files: 104
ASCII Value: 39, Character: ', Number of Files: 276
ASCII Value: 40, Character: (, Number of Files: 346
ASCII Value: 41, Character: ), Number of Files: 359
ASCII Value: 42, Character: \*, Number of Files: 128
ASCII Value: 43, Character: +, Number of Files: 146
ASCII Value: 44, Character: ,, Number of Files: 320
ASCII Value: 45, Character: -, Number of Files: 447
ASCII Value: 46, Character: ., Number of Files: 486
ASCII Value: 47, Character: /, Number of Files: 259
ASCII Value: 48, Character: 0, Number of Files: 2664
ASCII Value: 49, Character: 1, Number of Files: 2791
ASCII Value: 50, Character: 2, Number of Files: 2564
ASCII Value: 51, Character: 3, Number of Files: 2671
ASCII Value: 52, Character: 4, Number of Files: 2530
ASCII Value: 53, Character: 5, Number of Files: 2343
ASCII Value: 54, Character: 6, Number of Files: 2503
ASCII Value: 55, Character: 7, Number of Files: 2679
ASCII Value: 56, Character: 8, Number of Files: 2544
ASCII Value: 57, Character: 9, Number of Files: 2617
ASCII Value: 58, Character: :, Number of Files: 287
ASCII Value: 59, Character: ;, Number of Files: 223
ASCII Value: 60, Character: <, Number of Files: 168
ASCII Value: 61, Character: =, Number of Files: 254
ASCII Value: 62, Character: >, Number of Files: 162
ASCII Value: 63, Character: ?, Number of Files: 194
ASCII Value: 64, Character: @, Number of Files: 83
ASCII Value: 65, Character: A, Number of Files: 1923
ASCII Value: 66, Character: B, Number of Files: 1505
ASCII Value: 67, Character: C, Number of Files: 1644
ASCII Value: 68, Character: D, Number of Files: 1553
ASCII Value: 69, Character: E, Number of Files: 2171
ASCII Value: 70, Character: F, Number of Files: 1468
ASCII Value: 71, Character: G, Number of Files: 1443
ASCII Value: 72, Character: H, Number of Files: 1543
ASCII Value: 73, Character: I, Number of Files: 1888
ASCII Value: 74, Character: J, Number of Files: 1470
ASCII Value: 75, Character: K, Number of Files: 1504
ASCII Value: 76, Character: L, Number of Files: 1692
ASCII Value: 77, Character: M, Number of Files: 1484
ASCII Value: 78, Character: N, Number of Files: 1683
ASCII Value: 79, Character: O, Number of Files: 2097
ASCII Value: 80, Character: P, Number of Files: 1605
ASCII Value: 81, Character: Q, Number of Files: 1409
ASCII Value: 82, Character: R, Number of Files: 1811
ASCII Value: 83, Character: S, Number of Files: 1786
ASCII Value: 84, Character: T, Number of Files: 1729
ASCII Value: 85, Character: U, Number of Files: 1458
ASCII Value: 86, Character: V, Number of Files: 1405
ASCII Value: 87, Character: W, Number of Files: 1521
ASCII Value: 88, Character: X, Number of Files: 1366
ASCII Value: 89, Character: Y, Number of Files: 1456
ASCII Value: 90, Character: Z, Number of Files: 1451
ASCII Value: 91, Character: [, Number of Files: 111
ASCII Value: 93, Character: ], Number of Files: 104
ASCII Value: 94, Character: ^, Number of Files: 88
ASCII Value: 95, Character: \_, Number of Files: 80
ASCII Value: 96, Character: ', Number of Files: 42
ASCII Value: 97, Character: a, Number of Files: 2219
ASCII Value: 98, Character: b, Number of Files: 624
ASCII Value: 99, Character: c, Number of Files: 880
ASCII Value: 100, Character: d, Number of Files: 1074
ASCII Value: 101, Character: e, Number of Files: 2962
ASCII Value: 102, Character: f, Number of Files: 608
ASCII Value: 103, Character: g, Number of Files: 760
ASCII Value: 104, Character: h, Number of Files: 990
ASCII Value: 105, Character: i, Number of Files: 2035
ASCII Value: 106, Character: j, Number of Files: 427
ASCII Value: 107, Character: k, Number of Files: 557
ASCII Value: 108, Character: l, Number of Files: 1415
ASCII Value: 109, Character: m, Number of Files: 879
ASCII Value: 110, Character: n, Number of Files: 1906
ASCII Value: 111, Character: o, Number of Files: 2048
ASCII Value: 112, Character: p, Number of Files: 786
ASCII Value: 113, Character: q, Number of Files: 427
ASCII Value: 114, Character: r, Number of Files: 1708
ASCII Value: 115, Character: s, Number of Files: 1557
ASCII Value: 116, Character: t, Number of Files: 1781
ASCII Value: 117, Character: u, Number of Files: 1319
ASCII Value: 118, Character: v, Number of Files: 555
ASCII Value: 119, Character: w, Number of Files: 680
ASCII Value: 120, Character: x, Number of Files: 463
ASCII Value: 121, Character: y, Number of Files: 680
ASCII Value: 122, Character: z, Number of Files: 505
ASCII Value: 123, Character: {, Number of Files: 73
ASCII Value: 124, Character: |, Number of Files: 91
ASCII Value: 125, Character: }, Number of Files: 77
ASCII Value: 126, Character: ~, Number of Files: 59
ASCII Value: 999, Character: null, Number of Files: 4999



### Instance Description


Each dataset instance contains an image of a handwritten character or numeral, paired with its corresponding ASCII label. ### Data Organization


The dataset is organized into three separate .zip files: 'URL', 'URL', and 'URL'. Each ASCII symbol is housed in a dedicated folder, the name of which corresponds to the ASCII value of the symbol.


* 'URL' size: 55.9 MB
* 'URL' size: 16 MB
* 'URL' size: 8.06 MB


Dataset Utility
---------------


The AlphaNum dataset caters to a variety of use cases including text recognition, document processing, and machine learning tasks. It is particularly instrumental in the development, fine-tuning, and enhancement of OCR models.


Null Category Image Generation
------------------------------


The 'null' category comprises images generated by injecting noise to mimic randomly distributed light pixels. The creation of these images is accomplished through the following Python script:
This approach is particularly valuable as it enables the model to effectively disregard specific areas of the training data by utilizing a 'null' label. By doing so, the model becomes better at recognizing letters and can ignore irrelevant parts, enhancing its performance in reallive OCR tasks.


The 'null' labelled images in this dataset have been generated using the following algorithm.
(Please note that this is a non-deterministic approach, so you will most likely get different results.)


example: !noisy Image


ASCII Table and Corresponding File Counts
-----------------------------------------


ASCII Value: 33, Character: !, Number of Files: 207
ASCII Value: 34, Character: ", Number of Files: 267
ASCII Value: 35, Character: #, Number of Files: 152
ASCII Value: 36, Character: $, Number of Files: 192
ASCII Value: 37, Character: %, Number of Files: 190
ASCII Value: 38, Character: &, Number of Files: 104
ASCII Value: 39, Character: ', Number of Files: 276
ASCII Value: 40, Character: (, Number of Files: 346
ASCII Value: 41, Character: ), Number of Files: 359
ASCII Value: 42, Character: \*, Number of Files: 128
ASCII Value: 43, Character: +, Number of Files: 146
ASCII Value: 44, Character: ,, Number of Files: 320
ASCII Value: 45, Character: -, Number of Files: 447
ASCII Value: 46, Character: ., Number of Files: 486
ASCII Value: 47, Character: /, Number of Files: 259
ASCII Value: 48, Character: 0, Number of Files: 2664
ASCII Value: 49, Character: 1, Number of Files: 2791
ASCII Value: 50, Character: 2, Number of Files: 2564
ASCII Value: 51, Character: 3, Number of Files: 2671
ASCII Value: 52, Character: 4, Number of Files: 2530
ASCII Value: 53, Character: 5, Number of Files: 2343
ASCII Value: 54, Character: 6, Number of Files: 2503
ASCII Value: 55, Character: 7, Number of Files: 2679
ASCII Value: 56, Character: 8, Number of Files: 2544
ASCII Value: 57, Character: 9, Number of Files: 2617
ASCII Value: 58, Character: :, Number of Files: 287
ASCII Value: 59, Character: ;, Number of Files: 223
ASCII Value: 60, Character: <, Number of Files: 168
ASCII Value: 61, Character: =, Number of Files: 254
ASCII Value: 62, Character: >, Number of Files: 162
ASCII Value: 63, Character: ?, Number of Files: 194
ASCII Value: 64, Character: @, Number of Files: 83
ASCII Value: 65, Character: A, Number of Files: 1923
ASCII Value: 66, Character: B, Number of Files: 1505
ASCII Value: 67, Character: C, Number of Files: 1644
ASCII Value: 68, Character: D, Number of Files: 1553
ASCII Value: 69, Character: E, Number of Files: 2171
ASCII Value: 70, Character: F, Number of Files: 1468
ASCII Value: 71, Character: G, Number of Files: 1443
ASCII Value: 72, Character: H, Number of Files: 1543
ASCII Value: 73, Character: I, Number of Files: 1888
ASCII Value: 74, Character: J, Number of Files: 1470
ASCII Value: 75, Character: K, Number of Files: 1504
ASCII Value: 76, Character: L, Number of Files: 1692
ASCII Value: 77, Character: M, Number of Files: 1484
ASCII Value: 78, Character: N, Number of Files: 1683
ASCII Value: 79, Character: O, Number of Files: 2097
ASCII Value: 80, Character: P, Number of Files: 1605
ASCII Value: 81, Character: Q, Number of Files: 1409
ASCII Value: 82, Character: R, Number of Files: 1811
ASCII Value: 83, Character: S, Number of Files: 1786
ASCII Value: 84, Character: T, Number of Files: 1729
ASCII Value: 85, Character: U, Number of Files: 1458
ASCII Value: 86, Character: V, Number of Files: 1405
ASCII Value: 87, Character: W, Number of Files: 1521
ASCII Value: 88, Character: X, Number of Files: 1366
ASCII Value: 89, Character: Y, Number of Files: 1456
ASCII Value: 90, Character: Z, Number of Files: 1451
ASCII Value: 91, Character: [, Number of Files: 111
ASCII Value: 93, Character: ], Number of Files: 104
ASCII Value: 94, Character: ^, Number of Files: 88
ASCII Value: 95, Character: \_, Number of Files: 80
ASCII Value: 96, Character: ', Number of Files: 42
ASCII Value: 97, Character: a, Number of Files: 2219
ASCII Value: 98, Character: b, Number of Files: 624
ASCII Value: 99, Character: c, Number of Files: 880
ASCII Value: 100, Character: d, Number of Files: 1074
ASCII Value: 101, Character: e, Number of Files: 2962
ASCII Value: 102, Character: f, Number of Files: 608
ASCII Value: 103, Character: g, Number of Files: 760
ASCII Value: 104, Character: h, Number of Files: 990
ASCII Value: 105, Character: i, Number of Files: 2035
ASCII Value: 106, Character: j, Number of Files: 427
ASCII Value: 107, Character: k, Number of Files: 557
ASCII Value: 108, Character: l, Number of Files: 1415
ASCII Value: 109, Character: m, Number of Files: 879
ASCII Value: 110, Character: n, Number of Files: 1906
ASCII Value: 111, Character: o, Number of Files: 2048
ASCII Value: 112, Character: p, Number of Files: 786
ASCII Value: 113, Character: q, Number of Files: 427
ASCII Value: 114, Character: r, Number of Files: 1708
ASCII Value: 115, Character: s, Number of Files: 1557
ASCII Value: 116, Character: t, Number of Files: 1781
ASCII Value: 117, Character: u, Number of Files: 1319
ASCII Value: 118, Character: v, Number of Files: 555
ASCII Value: 119, Character: w, Number of Files: 680
ASCII Value: 120, Character: x, Number of Files: 463
ASCII Value: 121, Character: y, Number of Files: 680
ASCII Value: 122, Character: z, Number of Files: 505
ASCII Value: 123, Character: {, Number of Files: 73
ASCII Value: 124, Character: |, Number of Files: 91
ASCII Value: 125, Character: }, Number of Files: 77
ASCII Value: 126, Character: ~, Number of Files: 59
ASCII Value: 999, Character: null, Number of Files: 4999
TAGS
#task_categories-image-classification #size_categories-100K<n<1M #language-English #license-mit #OCR #Handwriting #Character Recognition #Grayscale Images #ASCII Labels #Optical Character Recognition #region-us 
 ### Instance Description


Each dataset instance contains an image of a handwritten character or numeral, paired with its corresponding ASCII label. ### Data Organization


The dataset is organized into three separate .zip files: 'URL', 'URL', and 'URL'. Each ASCII symbol is housed in a dedicated folder, the name of which corresponds to the ASCII value of the symbol.


* 'URL' size: 55.9 MB
* 'URL' size: 16 MB
* 'URL' size: 8.06 MB


Dataset Utility
---------------


The AlphaNum dataset caters to a variety of use cases including text recognition, document processing, and machine learning tasks. It is particularly instrumental in the development, fine-tuning, and enhancement of OCR models.


Null Category Image Generation
------------------------------


The 'null' category comprises images generated by injecting noise to mimic randomly distributed light pixels. The creation of these images is accomplished through the following Python script:
This approach is particularly valuable as it enables the model to effectively disregard specific areas of the training data by utilizing a 'null' label. By doing so, the model becomes better at recognizing letters and can ignore irrelevant parts, enhancing its performance in reallive OCR tasks.


The 'null' labelled images in this dataset have been generated using the following algorithm.
(Please note that this is a non-deterministic approach, so you will most likely get different results.)


example: !noisy Image


ASCII Table and Corresponding File Counts
-----------------------------------------


ASCII Value: 33, Character: !, Number of Files: 207
ASCII Value: 34, Character: ", Number of Files: 267
ASCII Value: 35, Character: #, Number of Files: 152
ASCII Value: 36, Character: $, Number of Files: 192
ASCII Value: 37, Character: %, Number of Files: 190
ASCII Value: 38, Character: &, Number of Files: 104
ASCII Value: 39, Character: ', Number of Files: 276
ASCII Value: 40, Character: (, Number of Files: 346
ASCII Value: 41, Character: ), Number of Files: 359
ASCII Value: 42, Character: \*, Number of Files: 128
ASCII Value: 43, Character: +, Number of Files: 146
ASCII Value: 44, Character: ,, Number of Files: 320
ASCII Value: 45, Character: -, Number of Files: 447
ASCII Value: 46, Character: ., Number of Files: 486
ASCII Value: 47, Character: /, Number of Files: 259
ASCII Value: 48, Character: 0, Number of Files: 2664
ASCII Value: 49, Character: 1, Number of Files: 2791
ASCII Value: 50, Character: 2, Number of Files: 2564
ASCII Value: 51, Character: 3, Number of Files: 2671
ASCII Value: 52, Character: 4, Number of Files: 2530
ASCII Value: 53, Character: 5, Number of Files: 2343
ASCII Value: 54, Character: 6, Number of Files: 2503
ASCII Value: 55, Character: 7, Number of Files: 2679
ASCII Value: 56, Character: 8, Number of Files: 2544
ASCII Value: 57, Character: 9, Number of Files: 2617
ASCII Value: 58, Character: :, Number of Files: 287
ASCII Value: 59, Character: ;, Number of Files: 223
ASCII Value: 60, Character: <, Number of Files: 168
ASCII Value: 61, Character: =, Number of Files: 254
ASCII Value: 62, Character: >, Number of Files: 162
ASCII Value: 63, Character: ?, Number of Files: 194
ASCII Value: 64, Character: @, Number of Files: 83
ASCII Value: 65, Character: A, Number of Files: 1923
ASCII Value: 66, Character: B, Number of Files: 1505
ASCII Value: 67, Character: C, Number of Files: 1644
ASCII Value: 68, Character: D, Number of Files: 1553
ASCII Value: 69, Character: E, Number of Files: 2171
ASCII Value: 70, Character: F, Number of Files: 1468
ASCII Value: 71, Character: G, Number of Files: 1443
ASCII Value: 72, Character: H, Number of Files: 1543
ASCII Value: 73, Character: I, Number of Files: 1888
ASCII Value: 74, Character: J, Number of Files: 1470
ASCII Value: 75, Character: K, Number of Files: 1504
ASCII Value: 76, Character: L, Number of Files: 1692
ASCII Value: 77, Character: M, Number of Files: 1484
ASCII Value: 78, Character: N, Number of Files: 1683
ASCII Value: 79, Character: O, Number of Files: 2097
ASCII Value: 80, Character: P, Number of Files: 1605
ASCII Value: 81, Character: Q, Number of Files: 1409
ASCII Value: 82, Character: R, Number of Files: 1811
ASCII Value: 83, Character: S, Number of Files: 1786
ASCII Value: 84, Character: T, Number of Files: 1729
ASCII Value: 85, Character: U, Number of Files: 1458
ASCII Value: 86, Character: V, Number of Files: 1405
ASCII Value: 87, Character: W, Number of Files: 1521
ASCII Value: 88, Character: X, Number of Files: 1366
ASCII Value: 89, Character: Y, Number of Files: 1456
ASCII Value: 90, Character: Z, Number of Files: 1451
ASCII Value: 91, Character: [, Number of Files: 111
ASCII Value: 93, Character: ], Number of Files: 104
ASCII Value: 94, Character: ^, Number of Files: 88
ASCII Value: 95, Character: \_, Number of Files: 80
ASCII Value: 96, Character: ', Number of Files: 42
ASCII Value: 97, Character: a, Number of Files: 2219
ASCII Value: 98, Character: b, Number of Files: 624
ASCII Value: 99, Character: c, Number of Files: 880
ASCII Value: 100, Character: d, Number of Files: 1074
ASCII Value: 101, Character: e, Number of Files: 2962
ASCII Value: 102, Character: f, Number of Files: 608
ASCII Value: 103, Character: g, Number of Files: 760
ASCII Value: 104, Character: h, Number of Files: 990
ASCII Value: 105, Character: i, Number of Files: 2035
ASCII Value: 106, Character: j, Number of Files: 427
ASCII Value: 107, Character: k, Number of Files: 557
ASCII Value: 108, Character: l, Number of Files: 1415
ASCII Value: 109, Character: m, Number of Files: 879
ASCII Value: 110, Character: n, Number of Files: 1906
ASCII Value: 111, Character: o, Number of Files: 2048
ASCII Value: 112, Character: p, Number of Files: 786
ASCII Value: 113, Character: q, Number of Files: 427
ASCII Value: 114, Character: r, Number of Files: 1708
ASCII Value: 115, Character: s, Number of Files: 1557
ASCII Value: 116, Character: t, Number of Files: 1781
ASCII Value: 117, Character: u, Number of Files: 1319
ASCII Value: 118, Character: v, Number of Files: 555
ASCII Value: 119, Character: w, Number of Files: 680
ASCII Value: 120, Character: x, Number of Files: 463
ASCII Value: 121, Character: y, Number of Files: 680
ASCII Value: 122, Character: z, Number of Files: 505
ASCII Value: 123, Character: {, Number of Files: 73
ASCII Value: 124, Character: |, Number of Files: 91
ASCII Value: 125, Character: }, Number of Files: 77
ASCII Value: 126, Character: ~, Number of Files: 59
ASCII Value: 999, Character: null, Number of Files: 4999
passage: TAGS
#task_categories-image-classification #size_categories-100K<n<1M #language-English #license-mit #OCR #Handwriting #Character Recognition #Grayscale Images #ASCII Labels #Optical Character Recognition #region-us 
### Instance Description


Each dataset instance contains an image of a handwritten character or numeral, paired with its corresponding ASCII label.