Abstract
In this study, an automatic receipt recognition system (ARRS) is developed. First, a receipt is scanned for conversion into a high-resolution image. Receipt characters are automatically placed into two categories according to the receipt characteristics: printed and handwritten characters. Images of receipts with these characters are preprocessed separately. For handwritten characters, template matching and the fixed features of the receipts are used for text positioning, and projection is applied for character segmentation. Finally, a convolutional neural network is used for character recognition. For printed characters, a modified You Only Look Once (version 4) model (YOLOv4-s) executes precise text positioning and character recognition. The proposed YOLOv4-s model reduces downsampling, thereby enhancing small-object recognition. Finally, the system produces recognition results in a tax declaration format, which can upload to a tax declaration system. Experimental results revealed that the recognition accuracy of the proposed system was 80.93% for handwritten characters. Moreover, the YOLOv4-s model had a 99.39% accuracy rate for printed characters; only 33 characters were misjudged. The recognition accuracy of the YOLOv4-s model was higher than that of the traditional YOLOv4 model by 20.57%. Therefore, the proposed ARRS can considerably improve the efficiency of tax declaration, reduce labor costs, and simplify operating procedures.
Highlights
Aprecaptured template image was used for template matching for the handwritten characters
This study developed an automatic receipt recognition system (ARRS) for improving operational efficiency and reducing human errors in the processing of receipt information for tax declaration tasks
A receipt is scanned into a high-resolution image, and the characters on the receipt are automatically classified into two categories according to the characteristics of the receipt characters: printed and handwritten receipts
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Improve work efficiency, and reduce human errors in tax-filing processes, an automatic system for recognizing receipt information must be developed. To improve operational efficiency and reduce human errors in the processing of receipt information for tax declaration tasks, the present study developed an automatic receipt recognition system (ARRS). The proposed ARRS involves the following operations: receipt image reading, data preprocessing, text positioning, character segmentation, character recognition, review and error correction, and database upload. This study developed an automatic receipt recognition system (ARRS) for recognizing printed and handwritten receipts. The ARRS includes receipt image reading, data preprocessing, text positioning, character segmentation, character recognition, review and error correction, and database upload function, which can help users quickly convert paper receipts into electronic files.
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