metadata

license: mit

AlphaNum Dataset

Abstract

The AlphaNum dataset, curated by Louis Rädisch, is an extensive repository of grayscale, handwritten characters and numerals, each of 28x28 pixel dimensions. This dataset is designed to support Optical Character Recognition (OCR) tasks, offering labels that range from 33 to 126, and 999, aligning with ASCII characters from '!' to '~', and 'null', respectively. The 'null' category includes images generated through a noise injection process, resulting in normally distributed light pixels placed randomly.

Images drawn from the MNIST dataset have undergone color inversion to ensure consistency throughout the dataset. Vision Transformer Models have been fine-tuned to unify data sourced from varied origins, thereby augmenting the overall accuracy of the dataset. Notably, the 'A-Z handwritten alphabets' dataset, which initially did not distinguish between upper and lower case letters, has been modified to correct this in the present compilation.

Data Sources

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, contained in a .rar file, is organized within a "dataset" folder. Each ASCII symbol is housed in a dedicated folder, the name of which corresponds to the ASCII value of the symbol.

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:

import os
import numpy as np
from PIL import Image, ImageOps, ImageEnhance

def generate_noisy_images(num_images, image_size=(28, 28), 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 5000 noisy images
generate_noisy_images(5000)

ASCII Table

ASCII Value	Character
33	!
34	"
35	#
36	$
37	%
38	&
39	'
40	(
41	)
42	*
43	+
44	,
45	-
46	.
47	/
48	0
49	1
50	2
51	3
52	4
53	5
54	6
55	7
56	8
57	9
58	:
59	;
60	<
61	=
62	>
63	?
64	@
65	A
66	B
67	C
68	D
69	E
70	F
71	G
72	H
73	I
74	J
75	K
76	L
77	M
78	N
79	O
80	P
81	Q
82	R
83	S
84	T
85	U
86	V
87	W
88	X
89	Y
90	Z
91	[
93	]
94	^
95	_
96	`
97	a
98	b
99	c
100	d
101	e
102	f
103	g
104	h
105	i
106	j
107	k
108	l
109	m
110	n
111	o
112	p
113	q
114	r
115	s
116	t
117	u
118	v
119	w
120	x
121	y
122	z
123	{
124	\|
125	}
126	~
999	null