## 1 Introduction
The proposed connectionist text proposal network (CTPN) is used to locate the text region in the receipt, then the convolutional recurrent neural network (CRNN) is adopted to convert the receipt from an image into text. [PERSON] [13] used YOLO V3 to identify the text from the triplicate uniform invoice and solve the cumbersome and error-prone problem of manual processing. [PERSON] [14] compared the effectiveness of towards document image quality assessment (TDIQA), Yolov5, seq2 seq, and transformer in invoice identification. They found that yolov5 has the highest accuracy among these methods, but it also requires more training time. The methods mentioned above recognize the computer-printed text in the receipt. If you encounter a handwritten receipt, the accuracy will drop significantly. 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. In Taiwan, receipts can be classified into two categories: printed and handwritten receipts. In the proposed system, these two types of receipts are processed separately, and their images are preprocessed. The system operates as follows: First, a high-resolution image of the receipt is created using a scanner. Subsequently, the receipt is automatically classified as a printed or handwritten receipt according to the features of its characters. For a handwritten receipt, template matching is applied, and the fixed features of the receipt are used for text positioning. Subsequently, character segmentation is executed through a projection method. A CNN is then used to perform character recognition. For a printed receipt, a YOLOv4-s model is used for precise positioning of the text and character recognition. The YOLOv4-s model reduces downsampling, thereby enhancing the ability to recognize small objects. Next,after the characters on the two types of receipts are recognized, the results are uploaded to a database, and interface review and error correction are performed. Finally, the system provides an output in a tax declaration format, which can be uploaded to a tax declaration system. The proposed ARRS can substantially improve the efficiency of tax declaration, reduce labor costs, and simplify processing procedures. The contributions of this study are summarized as follows:  
* 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. * To improve the ability to recognize small objects, a YOLOv4-s model is developed in this study. * The proposed ARRS system provides a human-machine interface to enable users to review the results and perform error correction. The remainder of this paper is organized as follows. Section 2 provides an overview of the proposed ARRS. Section 3 presents the experimental results and a comparison of a previously proposed YOLOv4 model with the YOLOv4-s model. Finally, Section 4 provides the conclusion.