Traditional and New ICT Spending and Its Impact on Economy

Erman, Nuša; Rojko, Katarina; Lesjak, Dušan

doi:10.1080/08874417.2020.1830007

Cited by 5 publications

(1 citation statement)

References 33 publications

(53 reference statements)

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“…The trust of machine's reliability is also another limitation in invoice handling. Information and communication technology investing has a long-term impact on corporate growth as well as an immediate impact on labor productivity, thus managers should strive for the newest technology 7 . As the information in every invoice are crucial, the automated invoice recognition will help to reduce limitation faced by manual invoice handling.…”

Section: Introductionmentioning

confidence: 99%

Invoices Classification Using Deep Features Based on SME Perspectives

Azman

Mohamed

Jamil³

2021

Preprint

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Automation is seen as a potential alternative in improving productivity in the twenty-first century. Invoicing is the essential foundation of accounting record keeping and serves as a critical foundation for law enforcement inspections by auditing agencies and tax authorities. With the rise of artificial intelligence, automated record keeping systems are becoming more widespread in major organizations, allowing them to do tasks in real time and with no effort as well as a decision-making tool. Despite the system's benefits, many small and medium-sized businesses, particularly in Malaysia, are hesitant to implement it. Invoices are mostly processed manually that prone to human errors and lower productivity of the company. Artificial intelligence will further improve automated invoice handling making it simpler and efficient for all levels of businesses especially the small and medium enterprise This study presents a deep learning approach on record keeping focusing on invoices recognition by detecting invoice image classification. The deep learning model used in this research including the classic architecture of Convolutional Neural Network and its other variation such as VGG-16, VGG-19 and ResNet-50. Besides that, the constrains and expectation of the system to be implemented in small and medium enterprise in Malaysia are also presented in the interview scores. The research highlighted a comparison result between deep learning model and the perspective of SME presented in the discussion section. ResNet-50 shows a significant value in both training and validation accuracy compared to the other models with 95.90% accuracy in training and 74.24% accuracy for validation data. Future work will look at the suggested other deep learning method and intelligence features to be implemented for a more efficient invoices recognition and for small and medium enterprise.

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Section: Introductionmentioning

confidence: 99%