metadata

license: mit

AlphaNum Dataset

Dataset Summary

The AlphaNum dataset, curated by Louis Rädisch, is a comprehensive collection of grayscale, handwritten characters and digits, each with dimensions of 28x28 pixels. The primary aim of this dataset is to aid Optical Character Recognition (OCR) tasks. The dataset encompasses labels ranging from 33 to 126, and 999 representing the ASCII characters from '!' to '~', and 'null' respectively. The 'null' category comprises images with normally distributed light pixels placed randomly.

Images derived from the MNIST dataset have been color inverted to maintain consistency with the rest of the data. Vision Transformer Models have been fine-tuned to harmonize the data from diverse sources, enhancing the dataset's accuracy. For instance, the 'A-Z handwritten alphabets' dataset originally did not differentiate between upper and lower case letters, an issue rectified in this new compilation.

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

Sources:

The dataset files have been scaled down to 24x24 pixels and recolored from white-on-black to black-on-white to ensure uniformity.

Dataset Structure

Data Instances

A single data instance in this dataset comprises an image of a handwritten character or digit, accompanied by its corresponding ASCII label.

Data Fields

'image': This field contains the image of the handwritten character or digit.
'label': This field provides the ASCII label corresponding to the character or digit in the image.

Data Splits

The dataset is bifurcated into training and test subsets to facilitate the building and evaluation of models.

Dataset Use

The AlphaNum dataset is apt for tasks associated with text recognition, document processing, and machine learning. It is particularly beneficial for constructing, fine-tuning, and enhancing OCR models.