DOI: 10.5281/zenodo.14962182
title: Image Dataset for Classification and Segmentation

description: The dataset provided here was used to train and test a digit classifier using an SVM model, which was later employed to recognize numerical sequences displayed on various equipment screens. To evaluate the algorithm's performance, we include a set of images containing different numerical configurations, varying in digit count.
description: File: Training_DATA_For_SVM_MODEL.npy

Description: NumPy structured array containing grayscale digit images and corresponding labels for training the SVM classifier.

Shape: (8568, ) -  (structured array with named fields)

Data Type:



('image', 'O') – NumPy array of pixels representing the grayscale image.

('chars', 'U8') – Character label of the image (one of: '0'–'9', '(', ')', '/', '\\', '?', '%', ' ').

('value', 'f8') – Numerical value associated with the character (used as the training label).

('cor_fundo', 'i2') – Background color.

('cor_algar', 'i2') – Foreground color.

('nivel_ruido', 'i2') – Noise level applied to the image.

('fonte', 'i2') – Identifier of the font used to generate the image.


Usage:

import numpy as np

# Load dataset
image_data = np.load("Training_DATA_For_SVM_MODEL.npy")

# Check shape
print(image_data.shape)  # (8568,)

# Check data type
print(image_data.dtype)  
# [('image', 'O'), ('chars', 'U8'), ('value', 'f8'), ('cor_fundo', 'i2'), ('cor_algar', 'i2'), ('nivel_ruido', 'i2'), ('fonte', 'i2')]

# Accessing an example image
last_image = image_data['image'][-1]  # Get last image
print(last_image.shape)  # (height, width)
description: File: Verification_Data_For_Number_Identificator_Model.npy

Description: NumPy structured array containing images of numerical sequences and their corresponding labels for verifying the number identification model.

Shape: (39120, ) -  (structured array with named fields)

Data Type:



('image', 'O') – NumPy array of pixels representing the grayscale image.

('True_Value', 'f8') – Actual numerical value displayed in the image.

('Ident_Value', 'f8') – Identified value (should be ignored).

('num_caract', 'i2') – Number of characters in the image.

('tipo', '<U50') – Type of digit and character configuration (see categories below).

('cor_fundo', 'i2') – Background color.

('cor_algar', 'i2') – Foreground color.

('nivel_ruido', 'i2') – Noise level applied to the image.

 Character configuration Type: ('tipo', '<U50')



Decimal Numbers 



'Decimal' – Example: "12.43"

'Decimal*60' – Same as 'Decimal' but with an added reflection effect (intensity 60).

'Dec. Com ()' – Decimal number enclosed in parentheses: "(12.43)".

'Dec. Com ()*60' – Same as 'Dec. Com ()', with a reflection effect.



Integer Numbers:



'Inteiro' – Example: "235"

'Inteiro*60' – Same as 'Inteiro', with a reflection effect.

'int. Com ()' – Integer number enclosed in parentheses: "(235)"

'int. Com ()*60' – Same as 'int. Com ()', with a reflection effect.

'int. Com /' – Integer number enclosed with /: "/235/"

'int. Com /*60' – Same as 'int. Com /', with a reflection effect.

'int. Com \\ ' – Integer number enclosed with \: "\235\"

'int. Com \\*60' – Same as 'int. Com \\ ', with a reflection effect.



Non-Numeric Images:



'nn com %' – Image containing the "%" character.

'nn com %*60' – Same as 'nn com %', with a reflection effect.

'nn com ?' – Image containing the "?" character.

'nn com ?*60' – Same as 'nn com ?', with a reflection effect.

'no num' – Image that does not contain a numerical value.

'no num*60' – Same as 'no num', with a reflection effect.






Usage:

import numpy as np

# Load dataset
image_data = np.load("Verification_Data_For_Number_Identificator_Model.npy")

# Check shape
print(image_data.shape)  # (39120,)

# Check data type
print(image_data.dtype)  
# [('image', 'O'), ('True_Value', 'f8'), ('Ident_Value', 'f8'), ('num_caract', 'i2'), ('tipo', '<U50'), ('cor_fundo', 'i2'), ('cor_algar', 'i2'), ('nivel_ruido', 'i2')]

# Accessing an example image
last_image = image_data['image'][-1]  # Get last image
print(last_image.shape)  # (height, width)