Classification of Invoice Images By Using Convolutional Neural Networks

Today, as the companies grow, the number of personnel working within the company and the number of supplier companies that the company works with are also increasing. In parallel with this increase, the amount of expenditure made on behalf of the company increases, and more invoices are created. Sin...

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Main Authors: Sait Ali Uymaz, Ömer Arslan
Format: Article
Language:English
Published: Çanakkale Onsekiz Mart University 2022-03-01
Series:Journal of Advanced Research in Natural and Applied Sciences
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Online Access:https://dergipark.org.tr/en/download/article-file/1828897
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author Sait Ali Uymaz
Ömer Arslan
author_facet Sait Ali Uymaz
Ömer Arslan
author_sort Sait Ali Uymaz
collection DOAJ
description Today, as the companies grow, the number of personnel working within the company and the number of supplier companies that the company works with are also increasing. In parallel with this increase, the amount of expenditure made on behalf of the company increases, and more invoices are created. Since the invoices must be kept for legal reasons, physical invoices are transferred to the digital environment. Since large companies have large numbers of invoices, labor demand is higher in digitalizing invoices. In addition, as the number of invoices to be transferred to digital media increases, the number of possible errors during entry becomes more. This paper aims to automate the transfer of invoices to the digital environment. In this study, invoices belonging to four different templates were used. Invoice images taken from a bank system were used for the first time in this study, and the original invoice dataset was prepared. Furthermore, two more datasets were obtained by applying preprocessing methods (Zero-Padding, Brightness Augmentation) on the original dataset. The Invoice classification system developed using Convolutional Neural Networks (CNN) architectures named LeNet-5, VGG-19, and MobileNetV2 was trained on three different data sets. Data preprocessing techniques such as correcting the curvature and aspect ratio of the invoices and image augmentation with variable brightness ratio were applied to create the data sets. The datasets created with preprocessing techniques have increased the classification success of the proposed models. With this proposed model, invoice images were automatically classified according to their templates using CNN architectures. In experimental studies, a classification success rate of 99.83% was achieved in training performed on the data set produced by the data augmentation method.
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spelling doaj-art-01d31d707ca144f487731b0304320dc82025-02-05T17:58:10ZengÇanakkale Onsekiz Mart UniversityJournal of Advanced Research in Natural and Applied Sciences2757-51952022-03-018182510.28979/jarnas.953634453Classification of Invoice Images By Using Convolutional Neural NetworksSait Ali Uymaz0https://orcid.org/0000-0003-2748-8483Ömer Arslan1https://orcid.org/0000-0003-3474-2988MÜHENDİSLİK VE DOĞA BİLİMLERİ FAKÜLTESİKONYA TEKNİK ÜNİVERSİTESİToday, as the companies grow, the number of personnel working within the company and the number of supplier companies that the company works with are also increasing. In parallel with this increase, the amount of expenditure made on behalf of the company increases, and more invoices are created. Since the invoices must be kept for legal reasons, physical invoices are transferred to the digital environment. Since large companies have large numbers of invoices, labor demand is higher in digitalizing invoices. In addition, as the number of invoices to be transferred to digital media increases, the number of possible errors during entry becomes more. This paper aims to automate the transfer of invoices to the digital environment. In this study, invoices belonging to four different templates were used. Invoice images taken from a bank system were used for the first time in this study, and the original invoice dataset was prepared. Furthermore, two more datasets were obtained by applying preprocessing methods (Zero-Padding, Brightness Augmentation) on the original dataset. The Invoice classification system developed using Convolutional Neural Networks (CNN) architectures named LeNet-5, VGG-19, and MobileNetV2 was trained on three different data sets. Data preprocessing techniques such as correcting the curvature and aspect ratio of the invoices and image augmentation with variable brightness ratio were applied to create the data sets. The datasets created with preprocessing techniques have increased the classification success of the proposed models. With this proposed model, invoice images were automatically classified according to their templates using CNN architectures. In experimental studies, a classification success rate of 99.83% was achieved in training performed on the data set produced by the data augmentation method.https://dergipark.org.tr/en/download/article-file/1828897convolutional neural networksdeep learningimage classificationinvoice
spellingShingle Sait Ali Uymaz
Ömer Arslan
Classification of Invoice Images By Using Convolutional Neural Networks
Journal of Advanced Research in Natural and Applied Sciences
convolutional neural networks
deep learning
image classification
invoice
title Classification of Invoice Images By Using Convolutional Neural Networks
title_full Classification of Invoice Images By Using Convolutional Neural Networks
title_fullStr Classification of Invoice Images By Using Convolutional Neural Networks
title_full_unstemmed Classification of Invoice Images By Using Convolutional Neural Networks
title_short Classification of Invoice Images By Using Convolutional Neural Networks
title_sort classification of invoice images by using convolutional neural networks
topic convolutional neural networks
deep learning
image classification
invoice
url https://dergipark.org.tr/en/download/article-file/1828897
work_keys_str_mv AT saitaliuymaz classificationofinvoiceimagesbyusingconvolutionalneuralnetworks
AT omerarslan classificationofinvoiceimagesbyusingconvolutionalneuralnetworks