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Journal articles on the topic 'Invoice recognition'
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1
Yao, Xunfeng, Hao Sun, Sijun Li, and Weichao Lu. "Invoice Detection and Recognition System Based on Deep Learning." Security and Communication Networks 2022 (January 25, 2022): 1–10. http://dx.doi.org/10.1155/2022/8032726.
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With the development of economy and information technology, a large amount of invoice information has been produced. As one of the important components of the industrial Internet of Things, the recognition of invoice information is urgent to realize its intelligent recognition. Most invoice issuing units basically adopt traditional manual identification methods for the processing of invoices. As the number of invoices increases, problems such as low efficiency in identifying invoice information, error-prone, and difficulty in ensuring security frequently appear. In response to the above problems, this paper designs and implements an invoice information recognition system based on deep learning. The system first solves the problems of low image contrast and lack of image due to poor lighting or noise effects by image preprocessing methods such as image graying and normalization. Second, a target detection and invoice recognition method based on the combination of YOLOv3 + CRNN two models is proposed, and an end-to-end invoice information recognition model is obtained. Finally, the model is used to develop an invoice detection and recognition system based on deep learning. Experiments have verified that the system has the characteristics of high recognition accuracy and high efficiency, which can accurately identify invoice content information and reduce the loss of manpower and material resources.
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Singh, Arti. "Automated Invoice Data Extraction: Advancements and Challenges in OCR-Based Approaches." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 06 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem35494.
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The Invoice Recognition System (IRS) is an innovative system that is poised to revolutionize automatic data extraction from invoices. The IRS expertly translates handwritten and printed invoice data into efficient digital formats, utilizing cutting-edge data capture techniques and Optical Character Recognition (OCR) technology. The IRS, built for businesses with huge invoice volumes, aims to significantly enhance financial and administrative productivity by reducing errors and eliminating the need for manual data entry. The system makes advantage of OCR precision to ensure that information is thoroughly interpreted and extracted, enhancing data accuracy and processing speed. The IRS appears to be a useful tool for improving complex financial and administrative procedures, thanks to its adaptability to various invoicing forms. Keywords: invoice recognition system,OCR(optical character recognition),Automation,Invoices.
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Xian, Xiao Ping. "Machine-Printed Invoice Number Based on Fuzzy Recognition." Applied Mechanics and Materials 214 (November 2012): 705–10. http://dx.doi.org/10.4028/www.scientific.net/amm.214.705.
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A new fuzzy recognition method of machine-printed invoice number based on neural network is presented. This method includes ten links: invoice number detection and separation of right on top of invoice, binarization, denoising, incline correction, extraction of invoice code numerals, window scaling, location standardization, thinning, extraction of numeral feature and fuzzy recognition based on BP neural network. Through testing, the recognition rate of this method can be over 99%.The recognition time of characters for character is less than 1 second, which means that the method is of more effective recognition ability and can better satisfy the real system requirements.
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Li, Zhijie, Wencan Tian, Changhua Li, Yunpeng Li, and Haoqi Shi. "A Structured Recognition Method for Invoices Based on StrucTexT Model." Applied Sciences 13, no. 12 (2023): 6946. http://dx.doi.org/10.3390/app13126946.
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Invoice recognition has long been an active research direction in the field of image recognition. Existing invoice recognition methods suffer from a low recognition rate for structured invoices, a slow recognition speed, and difficulty in mobile deployment. To address these issues, we propose an invoice-structured recognition method based on the StrucTexT model. This method uses the idea of knowledge distillation to speed up the recognition speed and compress the model size without reducing the model recognition rate; this is achieved using the teacher model StrucTexT to guide the student model StrucTexT_slim. The method can effectively solve the problems of slow model recognition speed and large model size that make mobile deployment difficult with traditional methods. Experimental results show that the proposed model achieves an accuracy rate of over 94% on the SROIE and FUNSD public datasets and over 95% on the self-built structured invoice dataset. In addition, the method is 30% faster than other models (YOLOv4, LeNet-5, and Tesseract-OCR) in terms of recognition speed, while the model size is compressed by about 20%.
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Han, Yoon-Sang, Hong-Il Seo, and Dong-Hoan Seo. "Study on handwritten invoice recognition system." Journal of Advanced Marine Engineering and Technology 47, no. 6 (2023): 411–18. http://dx.doi.org/10.5916/jamet.2023.47.6.411.
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Pan, Wenfu, Li Chen, and Ruxing Zhang. "Automatic Recognition of Financial Instruments Based on Anisotropic Partial Differential Equations." Advances in Mathematical Physics 2021 (October 28, 2021): 1–13. http://dx.doi.org/10.1155/2021/6529859.
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In this paper, anisotropic partial differential equations are used to conduct an indepth study and analysis of automatic recognition of financial bills. Firstly, it obtains the invoices of the group enterprise, uses scanning technology and related image recognition technology to capture, process, compress and slice the paper bill content, and then carries out data identification and verification of the image. It classifies the obtained electronic data information into bills, converts it into electronic information related to bills according to the corresponding categories of the bill template, and stores it in the bill table of the database to achieve the management operation of formatted electronic files. After categorizing the bills according to the electronic information of bills to match the business scenarios, financial journal vouchers can be generated according to the preconfigured voucher templates of the corresponding business scenarios, and the financial journal vouchers are converted into voucher messages using XML technology. Finally, we use agent technology to design middleware for heterogeneous financial systems to realize the function of communicating voucher messages to each other in different business systems. The system automatically extracts the key information of invoices through OCR technology and performs real-time verification and cyclical feedback to the verification results to the suppliers. The system has realized the intelligent management of the power company’s VAT invoices, thus greatly enhancing the efficiency of VAT invoice verification and settlement. The automatic tax invoice recognition system adopts a network structured tax invoice recognition model, which eliminates the cumbersome steps of character decomposition and character classification in traditional OCR character recognition. After several trials, it has obtained better experimental results in terms of recognition accuracy, with an accuracy rate of over 93% in the recognition of tax invoice data set.
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Et. al., Rekha M,. "Educational Training For Processing Invoice Of Vendor Identification And Payments Using Python-Tesseract." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 11 (2021): 224–28. http://dx.doi.org/10.17762/turcomat.v12i11.5864.
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The aim of the project is to recognize the invoices of receipts from various vendors, by using automated invoice processing using various learning educational tools. This automated invoice processing is far better than manual invoice processing, it saves a serious amount of time and money creating efficiencies and increasing the accuracy of captured data. Basically, the invoices were calculated from the scanned receipts by using python-tesseract software. Python- tesseract is an optical character recognition (OCR) tool for python. It will recognize and read the text embedded in images. So, this python-tesseract software extracts key information like bill or invoice number, amount etc.; from all receipts and imports the calculated invoices and total amount of all receipts which are given by vendors to the database.
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Baviskar, Dipali, Swati Ahirrao, and Ketan Kotecha. "Multi-Layout Invoice Document Dataset (MIDD): A Dataset for Named Entity Recognition." Data 6, no. 7 (2021): 78. http://dx.doi.org/10.3390/data6070078.
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The day-to-day working of an organization produces a massive volume of unstructured data in the form of invoices, legal contracts, mortgage processing forms, and many more. Organizations can utilize the insights concealed in such unstructured documents for their operational benefit. However, analyzing and extracting insights from such numerous and complex unstructured documents is a tedious task. Hence, the research in this area is encouraging the development of novel frameworks and tools that can automate the key information extraction from unstructured documents. However, the availability of standard, best-quality, and annotated unstructured document datasets is a serious challenge for accomplishing the goal of extracting key information from unstructured documents. This work expedites the researcher’s task by providing a high-quality, highly diverse, multi-layout, and annotated invoice documents dataset for extracting key information from unstructured documents. Researchers can use the proposed dataset for layout-independent unstructured invoice document processing and to develop an artificial intelligence (AI)-based tool to identify and extract named entities in the invoice documents. Our dataset includes 630 invoice document PDFs with four different layouts collected from diverse suppliers. As far as we know, our invoice dataset is the only openly available dataset comprising high-quality, highly diverse, multi-layout, and annotated invoice documents.
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Ming, Delie, Jian Liu, and Jinwen Tian. "Research on Chinese financial invoice recognition technology." Pattern Recognition Letters 24, no. 1-3 (2003): 489–97. http://dx.doi.org/10.1016/s0167-8655(02)00271-4.
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Vijaya, Dr V. Krishna. "INVOICE DATA EXTRACTION USING OCR TECHNIQUE." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem29981.
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Traditional invoice processing involves manual entry of data, leading to human errors, delays,and increased operational costs. The lack of automation results in inefficiencies, hindering organizations from promptly accessing critical financial information. This research addresses the pressing need for a reliable OCR-based solution to automate invoice data extraction, ultimately improving accuracy, reducing processing time, and enhancing overall business productivity. The project aims to automate invoice data extraction through Optical Character Recognition (OCR) techniques. Leveraging advanced image processing and machine learning, the system will analyze scanned or photographed invoices, extracting relevant information such as vendor details, itemized costs, and dates.This automation streamlines manual data entry processes, enhancing accuracy and efficiency in managing financial records. OCR invoicing is the process of training a template-based OCR model for specific invoice layouts, setting up input paths for these invoices, extracting data, and integrating the extracted data with a structured database. Keywords: Invoice, OCR, YOLO algorithm, Data Extraction, Image Processing, Database Integration.
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Gawade, Lata. "Invoice data Extraction Using LLM and OCR." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 2953–57. https://doi.org/10.22214/ijraset.2025.70815.
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Abstract: Invoice processing is a crucial but time-consuming task for businesses, especially when done manually. It often leads to errors and inefficiencies, particularly for companies dealing with large volumes of documents. To solve this, automated data extraction systems use Optical Character Recognition (OCR) and Large Language Models (LLM) APIs. OCR converts scanned invoices into readable text, extracting details like invoice numbers, dates, and amounts. LLMs improve accuracy by understanding the context, handling uncertainties, and automating decisions. Together, OCR and LLMs streamline invoice workflows, cut costs, and speed up processing, making them valuable for financial operations across industries.
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Akanksh, Aparna Manjunath, Sudhakar Nayak Manjunath, Nishith Santhanam, et al. "Automated invoice data extraction using image processing." International Journal of Artificial Intelligence (IJ-AI) 12, no. 2 (2023): 514–21. https://doi.org/10.11591/ijai.v12.i2.pp514-521.
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Manually processing invoices which are in the form of scanned photocopies is a time-consuming process. There is a need to automate the task of extraction of data from the invoices with a similar format. In this paper we investigate and analyse various techniques of image processing and text extraction to improve the results of the optical character recognition (OCR) engine, which is applied to extract the text from the invoice. This paper also proposes the design and implementation of a web enabled invoice processing system (IPS). The IPS consists of an annotation tool and an extraction tool. The annotation tool is used to mark the fields of interest in the invoice which are to be extracted. The extraction tool makes use of opensource computer vision library (OpenCV) algorithms to detect text. The proposed system was tested on more than 25 types of invoices with the average accuracy score lying between 85% and 95%. Finally, to provide ease of use, a web application is developed which also presents the results in a structured format. The entire system is designed so as to provide flexibility and automate the process of extracting details of interest from the invoices.
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Lee, Jin-Se, Kyu-Wan Han, Hong-Il Seo, and Dong-Hoan Seo. "A study on automated invoice recognition and text correction." Journal of Advanced Marine Engineering and Technology 48, no. 6 (2024): 516–23. https://doi.org/10.5916/jamet.2024.48.6.516.
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Xie, Rongna, Weihua Mao, and Guozhen Shi. "Electronic Invoice Authenticity Verifying Scheme Based on Signature Recognition." Journal of Physics: Conference Series 1213 (June 2019): 032019. http://dx.doi.org/10.1088/1742-6596/1213/3/032019.
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Kurniawan, Benny, and Radius Tanone. "Implementation of Google Cloud in Business to Business Tanda Tukar Faktur Application." JUITA: Jurnal Informatika 9, no. 2 (2021): 201. http://dx.doi.org/10.30595/juita.v9i2.10321.
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Alfamart is a company engaged in retail. Companies involved in the retail sector are certainly inseparable from buying and selling products, and every transaction that occurs will be detailed in the invoice exchange. The problem that arises is because Alfamart wants to accommodate the electronic invoice exchange process. Therefore, Alfamart built a B2B TTF application that can accommodate the electronic invoice exchange process and help its accounting management. The application is made using the Research and Development method because it can address urgent needs and has a high validation value. It is built using the Flask framework and is integrated with Google Cloud to overcome application deployment speed problems and be more flexible. In addition, the implementation of Optical Character Recognition using Google Vision is used to validate uploaded invoice files. This study's results are in the form of a B2B TTF application that can make it easier for users to exchange invoices. The results of using Google Vision have a relatively high percentage of 77%. The B2B TTF application uses the Flask framework and is integrated with Google Cloud, which can assist users in the process of exchanging invoices electronically.
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Manjunath, Akanksh Aparna, Manjunath Sudhakar Nayak, Santhanam Nishith, et al. "Automated invoice data extraction using image processing." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 2 (2023): 514. http://dx.doi.org/10.11591/ijai.v12.i2.pp514-521.
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Manually processing invoices which are in the form of scanned photocopies is a time-consuming process. There is a need to automate the task of extraction of data from the invoices with a similar format. In this paper we investigate and analyse various techniques of image processing and text extraction to improve the results of the optical character recognition (OCR) engine, which is applied to extract the text from the invoice. This paper also proposes the design and implementation of a web enabled invoice processing system (IPS). The IPS consists of an annotation tool and an extraction tool. The annotation tool is used to mark the fields of interest in the invoice which are to be extracted. The extraction tool makes use of opensource computer vision library (OpenCV) algorithms to detect text. The proposed system was tested on more than 25 types of invoices with the average accuracy score lying between 85% and 95%. Finally, to provide ease of use, a web application is developed which also presents the results in a structured format. The entire system is designed so as to provide flexibility and automate the process of extracting details of interest from the invoices.
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Liang, Feiran, Yu Wu, and Jialun Yang. "Unveiling the potential of pre-processing and differentiable binarization thresholds for invoice text detection." Applied and Computational Engineering 6, no. 1 (2023): 1253–62. http://dx.doi.org/10.54254/2755-2721/6/20230654.
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Invoice text detection aims to automatically identify key information in invoices, which is one of the representative applications of Optical Character Recognition (OCR) technology and a research hotspot in the computer vision community. The classic traditional OCR method consists of three key steps: preprocessing stage, text detection and text recognition. In the feature extraction stage, it faces problems such as insufficient generalization ability and poor robustness. In this paper, we reveal the great potential of preprocessing strategies in improving OCR accuracy, and propose an invoice text detection method based on DBNet and differentiable binarization thresholds. Specifically, we first introduce color segmentation and local adaptive threshold to improve the preprocessing process, which can effectively suppress the influence of background information and context noise on detection results. In addition, a differentiable binarization threshold is introduced in the feature extraction, which improves the error correction speed in the whole deep learning process. Studies have shown significant improvements in test results after color segmentation or background removal compared to the original image. Therefore, we propose to properly consider the text detection of invoices in the preprocessing stage for improvement.
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Yi, Fei, Yi-Fei Zhao, Guan-Qun Sheng, et al. "Dual Model Medical Invoices Recognition." Sensors 19, no. 20 (2019): 4370. http://dx.doi.org/10.3390/s19204370.
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Hospitals need to invest a lot of manpower to manually input the contents of medical invoices (nearly 300,000,000 medical invoices a year) into the medical system. In order to help the hospital save money and stabilize work efficiency, this paper designed a system to complete the complicated work using a Gaussian blur and smoothing–convolutional neural network combined with a recurrent neural network (GBS-CR) method. Gaussian blur and smoothing (GBS) is a novel preprocessing method that can fix the breakpoint font in medical invoices. The combination of convolutional neural network (CNN) and recurrent neural network (RNN) was used to raise the recognition rate of the breakpoint font in medical invoices. RNN was designed to be the semantic revision module. In the aspect of image preprocessing, Gaussian blur and smoothing were used to fix the breakpoint font. In the period of making the self-built dataset, a certain proportion of the breakpoint font (the font of breakpoint is 3, the original font is 7) was added, in this paper, so as to optimize the Alexnet–Adam–CNN (AA-CNN) model, which is more suitable for the recognition of the breakpoint font than the traditional CNN model. In terms of the identification methods, we not only adopted the optimized AA-CNN for identification, but also combined RNN to carry out the semantic revisions of the identified results of CNN, meanwhile further improving the recognition rate of the medical invoices. The experimental results show that compared with the state-of-art invoice recognition method, the method presented in this paper has an average increase of 10 to 15 percentage points in recognition rate.
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Ming, Delie, Jian Liu, and Jinwen Tian. "The Design and Implementation of a Chinese Financial Invoice Recognition System." Journal of Quantitative Linguistics 9, no. 1 (2002): 19–33. http://dx.doi.org/10.1076/jqul.9.1.19.8484.
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Koti Reddy Onteddu. "AI-Powered Invoice Automation in ERP Systems: Revolutionizing Accounts Payable." Journal of Computer Science and Technology Studies 7, no. 5 (2025): 588–97. https://doi.org/10.32996/jcsts.2025.7.5.64.
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The integration of artificial intelligence with enterprise resource planning systems has revolutionized accounts payable processes, particularly in invoice automation. Modern organizations are increasingly adopting AI-powered solutions to streamline their financial operations, reduce manual intervention, and enhance accuracy in invoice processing. These implementations leverage advanced technologies such as machine learning, optical character recognition, and predictive analytics to transform traditional manual processes into efficient automated workflows. The transformation extends beyond mere automation, incorporating intelligent data extraction, validation, and matching capabilities while providing real-time financial insights and cash flow optimization. Organizations implementing these solutions experience substantial improvements in processing efficiency, cost reduction, and vendor relationship management. The implementation considerations encompass both technical aspects of system integration and organizational change management, leading to measurable benefits in both quantitative performance metrics and qualitative operational improvements. As technology continues to evolve, the integration of blockchain and advanced AI capabilities promises to further enhance invoice processing capabilities, setting new standards for financial operations management.
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Alan Jiju, Shaun Tuscano, and Chetana Badgujar. "OCR Text Extraction." International Journal of Engineering and Management Research 11, no. 2 (2021): 83–86. http://dx.doi.org/10.31033/ijemr.11.2.11.
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This research tries to find out a methodology through which any data from the daily-use printed bills and invoices can be extracted. The data from these bills or invoices can be used extensively later on – such as machine learning or statistical analysis. This research focuses on extraction of final bill-amount, itinerary, date and similar data from bills and invoices as they encapsulate an ample amount of information about the users purchases, likes or dislikes etc. Optical Character Recognition (OCR) technology is a system that provides a full alphanumeric recognition of printed or handwritten characters from images. Initially, OpenCV has been used to detect the bill or invoice from the image and filter out the unnecessary noise from the image. Then intermediate image is passed for further processing using Tesseract OCR engine, which is an optical character recognition engine. Tesseract intends to apply Text Segmentation in order to extract written text in various fonts and languages. Our methodology proves to be highly accurate while tested on a variety of input images of bills and invoices.
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Liu, Zhiyin. "Accounting-Oriented Research on Note Recognition Model based on Information Extraction Algorithm." WSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS 21 (December 26, 2024): 2640–52. https://doi.org/10.37394/23207.2024.21.216.
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Enterprise accountants deal with bill reimbursement mostly relying on the traditional manual way to carry out, and the current bill recognition technology makes it difficult to meet the recognition needs of Chinese bills. And there is a lack of open-source Chinese bill recognition models in the training and validation process of the billing model. Aiming at the above challenges, the study proposes an information extraction algorithm based on the optical character recognition technique of deep learning, and the bill recognition model construction is carried out on this basis. Image detection is performed by utilizing detection and recognition neural networks, and image feature extraction is performed by combining convolutional recurrent neural networks with connectionist temporal classification. The validation shows that the accuracy of the research-proposed information extraction algorithm increases by an average of 9.86% compared with other algorithms in the self-constructed cab invoice dataset, and the F1 value in the International Conference on Integration and Innovation of Digital Archival Resources Toward the Enhancement of Public Service Capability 2015 dataset increases by 5.82% and 0.92% compared with other algorithms, respectively. Compared to other models, the study’s proposed model increases the average number of frames per second by 34.47% and the average class-wide accuracy by 10.72% in the cab invoice dataset. The bill recognition model based on the information extraction algorithm proposed in the study can meet the bill recognition requirements, has superior recognition accuracy and efficiency, and has application value in enterprise bill recognition.
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Ranadheer Reddy Charabuddi. "Transforming financial operations: Integrating SAP OpenText VIM, AI-Powered OCR, and RPA for advanced invoice processing and junk document elimination." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 828–36. https://doi.org/10.30574/wjaets.2025.15.2.0620.
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This article presents an integrated technological framework that addresses critical challenges in enterprise financial management through the convergence of SAP OpenText Vendor Invoice Management (VIM), Artificial Intelligence-powered Optical Character Recognition (AI OCR), and Robotic Process Automation (RPA). The solution tackles persistent issues including manual processing inefficiencies, high error rates, compliance risks, and the problematic processing of irrelevant documents. By orchestrating these advanced technologies, organizations can achieve streamlined invoice workflows, enhanced data extraction precision, automated verification processes, and complete elimination of junk document processing. The human-AI partnership remains central to this approach, with financial experts focusing on strategic decisions while automation handles routine transactions. This transformative model delivers substantial improvements in operational efficiency, processing speed, cost reduction, and compliance management, positioning enterprises for sustainable competitive advantage in financial operations.
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Hu, Shuyu. "Research on Data Acquisition Algorithms Based on Image Processing and Artificial Intelligence." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 06 (2019): 2054016. http://dx.doi.org/10.1142/s0218001420540166.
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At present, image recognition processing technology has been playing a decisive role in the field of pattern recognition, of which automatic recognition of bank notes is an important research topic. Due to the limitation of the size of bill layout and printing method, many invoice layouts are not clear, skewed or distorted, and even there are irregular handwritten signature contents, which lead to the problem of recognition of digital characters on bill surface. In this regard, this paper proposes a data acquisition and recognition algorithm based on improved BP neural network for ticket number identification, which is based on the theory of image processing and recognition, combined with improved bill information recognition technology. First, in the pre-processing stage of bill image, denoising and graying of bill image are processed. After binarization of bill image, the tilt detection method based on Bresenham integer algorithm is used to correct the tilted bill image. Secondly, character localization and feature extraction are carried out for par characters, and the target background is separated from the interference background in order to extract the desired target characters. Finally, the improved BP neural network-based bill digit data acquisition and recognition algorithm is used to realize the classification and recognition of bill characters. The experimental results show that the improved method has better classification and recognition effect than other data acquisition and recognition algorithms.
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Rahul Kiran Talaseela. "Cognitive Automation Using Natural Language and Optical Character Recognition." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 3 (2025): 193–209. https://doi.org/10.32628/cseit2511319.
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Cognitive automation technologies, particularly Natural Language Processing (NLP) and Optical Character Recognition (OCR), are revolutionizing how organizations handle unstructured data. This article explores how these technologies transform business operations by automating the interpretation, extraction, and conversion of unstructured information from documents, emails, and contracts into actionable intelligence. The integration of these cognitive capabilities enables organizations to process document-intensive workflows with greater speed, accuracy, and consistency while reducing operational costs. Implementation examples from finance and legal departments demonstrate significant performance improvements in invoice processing, receipt management, purchase order matching, and contract analysis. The technical architecture supporting these capabilities features modular components that work together to ingest, pre-process, recognize, interpret, and integrate document information into business systems. Despite implementation challenges related to data quality, training requirements, and system integration, organizations adopting these technologies report substantial returns through increased efficiency, improved accuracy, faster processing, enhanced compliance, greater scalability, and reduced costs.
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Ha, H. T., and A. Horák. "Information extraction from scanned invoice images using text analysis and layout features." Signal Processing: Image Communication 102 (March 2022): 116601. http://dx.doi.org/10.1016/j.image.2021.116601.
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Fan, Di, Zhe Chen, Chang Cun Bu, Zhun Sheng Yang, and Jia Li. "Recognizing Check Magnetic Code Based on Peak-Valley Location and Amplitude." Applied Mechanics and Materials 249-250 (December 2012): 241–46. http://dx.doi.org/10.4028/www.scientific.net/amm.249-250.241.
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Magnetic code is widely used in check, securities, tax invoice, etc. However, the traditional recognizing and reading method of magnetic code is mostly based on correlation coefficient and it takes significant time and cost. After analyzing the characteristics of magnetic code signals in E-13B standard, this paper has proposed a new algorithm based on the peak-valley location and amplitude (PVLA) to simplify the calculation and system design. Firstly, the magnetic code signal is separated into magnetic ink character signals by the thresholds of peak and valley. Secondly, the features of the peak-valley location (PVL) and peak-valley amplitude(PVA) of each magnetic ink character signal are extracted and normalized, then the nearest neighbor recognition algorithm based on the vectors of peak-valley location and amplitude is utilized to recognize the magnetic code. The recognition results and statistical parameters from a large number of experiments show that the new method has higher recognition rate and better robustness. In addition, the new algorithm only involves additions and subtractions, so it has a lower computation cost.
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Schweinberger, Stefan R., Anja Herholz, and Volker Stief. "Auditory Long term Memory: Repetition Priming of Voice Recognition." Quarterly Journal of Experimental Psychology Section A 50, no. 3 (1997): 498–517. http://dx.doi.org/10.1080/713755724.
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Two experiments examined repetition priming in the recognition of famous voices. In Experiment 1, reaction times for fame decisions to famous voice samples were shorter than in an unprimed condition, when voices were primed by a different voice sample of the same person having been presented in an earlier phase of the experiment. No effect of voice repetition was observed for non-famous voices. In Experiment 2, it was investigated whether this priming effect is voice-specific or whether it is related to post-perceptual processes in person recognition. Recognizing a famous voice was again primed by having earlier heard a different voice sample of that person. Although an earlier exposure to that person's name did not cause any priming, there was some indication of priming following an earlier exposure to that person's face. Finally, earlier exposure to the identical voice sample (as compared to a different voice sample from the same person) caused a considerable bias towards responding “famous”—i.e. performance benefits for famous but costs for nonfamous voices. The findings suggest that (1) repetition priming invoice recognition primarily involves the activation of perceptual representations of voices, and (2) it is important to determine the conditions in which priming causes bias effects that need to be disentangled from performance benefits.
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Azzam, Fatima, Mariam Jaber, Amany Saies, et al. "The Use of Blockchain Technology and OCR in E-Government for Document Management: Inbound Invoice Management as an Example." Applied Sciences 13, no. 14 (2023): 8463. http://dx.doi.org/10.3390/app13148463.
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The field of electronic government (e-government) is gaining prominence in contemporary society, as it has a significant influence on the wider populace within the context of a technologically advanced world. E-government makes use of information and communication technologies (ICTs) at various levels and domains within government agencies and the public sector. ICT reduces manual labour, potential fraud points, errors, and process lapses. The Internet’s quick accessibility and the widespread adoption of modern technologies and disciplines, such as big data, the Internet of Things, machine learning, and artificial intelligence, have accelerated the need for e-government. However, these developments raise a number of data reliability and precision concerns. The adoption of blockchain technology by researchers demonstrates its efficacy in addressing such issues. The present study proposes the SECHash system model, which integrates blockchain and Optical Character Recognition (OCR) technologies for the purpose of regulating the processing of incoming documents by governmental agencies. As a case study to assess the proposed system paradigm, the study uses a document containing incoming invoices. The proposal seeks to maintain the integrity of document data by prohibiting its modification after acceptance. Additionally, SECHash guarantees that accepted documents will not be destroyed or lost. The analysis demonstrates that using the SECHash model system will decrease fraudulent transactions by eradicating manual labour and storing documents on a blockchain network.
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Wang, Tao, and Min Qiu. "A visual transformer-based smart textual extraction method for financial invoices." Mathematical Biosciences and Engineering 20, no. 10 (2023): 18630–49. http://dx.doi.org/10.3934/mbe.2023826.
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<abstract><p>In era of big data, the computer vision-assisted textual extraction techniques for financial invoices have been a major concern. Currently, such tasks are mainly implemented via traditional image processing techniques. However, they highly rely on manual feature extraction and are mainly developed for specific financial invoice scenes. The general applicability and robustness are the major challenges faced by them. As consequence, deep learning can adaptively learn feature representation for different scenes and be utilized to deal with the above issue. As a consequence, this work introduces a classic pre-training model named visual transformer to construct a lightweight recognition model for this purpose. First, we use image processing technology to preprocess the bill image. Then, we use a sequence transduction model to extract information. The sequence transduction model uses a visual transformer structure. In the stage target location, the horizontal-vertical projection method is used to segment the individual characters, and the template matching is used to normalize the characters. In the stage of feature extraction, the transformer structure is adopted to capture relationship among fine-grained features through multi-head attention mechanism. On this basis, a text classification procedure is designed to output detection results. Finally, experiments on a real-world dataset are carried out to evaluate performance of the proposal and the obtained results well show the superiority of it. Experimental results show that this method has high accuracy and robustness in extracting financial bill information.</p></abstract>
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Kevin, Timotius, Fernaldi Kurniawan, Victor Sutiono, and Vina Georgiana*. "Design of Participants Registration System for Training and Consulting Institution." International Journal of Innovative Technology and Exploring Engineering 9, no. 4 (2020): 940–44. http://dx.doi.org/10.35940/ijrte.d1443.018520.
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The rapid development information technology and the need for recognition of the workforce competencies, creating challenges for the growth of consulting and training institution. In this study, efforts will be made to improve participants registration system through the design of application. The author uses ITIL v3 problem management to analyze existing problems in the company and use the Object-Oriented Analysis and Design (OOAD) method in developing the systems. Data collection is done by conducting surveys, document studies, interviews and observations. The results of this study are a web-based registration system design that has the advantages of functional features, that is, Join Training, Invoice and Registration Status, where these three features will facilitate participants in registering, paying for and training placement. With the system developed, the registration process can run more effectively and efficiently than ever before and improve business operations at the institution.
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Riba, Pau. "Distilling Structure from Imagery:Graph-based Models for the Interpretation of Document Images." ELCVIA Electronic Letters on Computer Vision and Image Analysis 19, no. 2 (2021): 9–10. http://dx.doi.org/10.5565/rev/elcvia.1313.
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From its early stages, the community of Pattern Recognition and Computer Vision has considered the importance of leveraging the structural information when understanding images. Usually, graphs have been proposed as a suitable model to represent this kind of information due to their flexibility and representational power able to codify both, the components, objects, or entities and their pairwise relationship. Even though graphs have been successfully applied to a huge variety of tasks, as a result of their symbolic and relational nature, graphs have always suffered from some limitations compared to statistical approaches. Indeed, some trivial mathematical operations do not have an equivalence in the graph domain. For instance, in the core of many pattern recognition applications, there is a need to compare two objects. This operation, which is trivial when considering feature vectors defined in ℝn, is not properly defined for graphs. In this thesis, we have investigated the importance of the structural information from two perspectives, the traditional graph-based methods and the new advances on Geometric Deep Learning. On the one hand, we explore the problem of defining a graph representation and how to deal with it on a large scale and noisy scenario. On the other hand, Graph Neural Networks are proposed to first redefine a Graph Edit Distance methodologies as a metric learning problem, and second, to apply them in a real use case scenario for the detection of repetitive patterns which define tables in invoice documents. As experimental framework, we have validated the different methodological contributions in the domain of Document Image Analysis and Recognition.
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Liang, Qiao, Li Zaisheng, Cheng Zhanzhan, and Li Xi. "SCID：a Chinese characters invoice-scanned dataset in relevant to key information extraction derived of visually-rich document images." Journal of Image and Graphics 28, no. 8 (2023): 2298–313. http://dx.doi.org/10.11834/jig.220911.
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Kandyba, V., O. Kushnir, V. Bredikhin, and I. Khoroshylova. "STUDY OF MACHINE LEARNING TOOLS AND ALGORITHMS FOR RECOGNITION AND DIGITALISATION OF SALES RECEIPTS." Municipal economy of cities 6, no. 180 (2023): 7–11. http://dx.doi.org/10.33042/2522-1809-2023-6-180-7-11.
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This article discusses the issue of processing images of sales receipts for subsequent text information extraction using OCR methods. This application is helpful for maintaining a family budget or for conducting accounting in small companies. The main problem with recognising receipts is the low quality of ink and printing paper, which is why it wrinkles and tears easily, and printed letters quickly fade. The study is based on a series of algorithms based on stepwise methods and integrated image transformation methods that can significantly improve the resulting character recognition. The step-by-step methods localise the text, carry out classification, segmentation, and text recognition, and remove the background part at each algorithm stage. Since they do not depend on the size of the dictionary, they can be used to recognise text from images regardless of its size. To solve the problem, we proposed a unique algorithm for image normalisation, which includes finding a receipt in the image, processing the resulting image area, removing shooting defects and media defects, and using a neural network to process and restore characters. We used the EAST (Efficient and Accurate Scene Text Detector) algorithm implemented using a convolutional neural network (CNN) for the text-finding process. Based on a comparison of the performance of the models in terms of their size and H-mean value, we selected the ddrnet23-slim neural network for the test images. The developed application can significantly increase the accuracy of text information recognition and, simultaneously, is small in size. The developed system recognises characters with reasonably high accuracy and shows the accuracy of the recognition result at a level of 97% and higher. The proposed system can be used: to detect and recognise characters by automatically scanning and updating invoice fields in the database; to extract text from an image and automatically convert it to digital format and update it in the database; as a tool for detecting, recognising, and understanding texts. Keywords: dataset, neural network, digital technologies, binarisation, sales receipt, classification, OCR.
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Scott, Conrad, Portia Priegert, Darren Patrick, and Frank Frances. "The End of the Beginning: Environmental Apocalypse on the Cusp in Scott Fotheringham’s The Rest is Silence and Nicolas Dickner’s Apocalypse for Beginners; InVoice; Road’s End; & The Outside." UnderCurrents: Journal of Critical Environmental Studies 18 (April 27, 2014): 28–37. http://dx.doi.org/10.25071/2292-4736/38543.
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Scott Fotheringham’s novel The Rest is Silence and Nicolas Dickner’s novel Apocalypse for Beginners both mix coming-of-age narratives with environmental destruction through apocalyptic events. Similarly, concerns about global environmental destruction populate the bildungsroman in fiction from nuclear-era texts such as John Wyndham’s The Chrysalids to ongoing narratives such as Margaret Atwood’s MaddAddam series. However, both Fotheringham’s The Rest is Silence and Dickner’s Apocalypse for Beginners integrate coming-of-age narratives with apocalyptic threats to the characters’ environments that climax at the edge of a point-of-no-return and then subside without having completely eradicated the living environment beyond recognition. The two novels represent a rethinking of how apocalyptic threat effects the world: these texts reject the idea of immediate doom represented by, for example, fiction focused on nuclear destruction, and the notion “’[t]hat there’s no problem that can’t be fixed with a good old end of the world’” (Dickner 89).....inVoiceimountainssoured with bitterroot stumps at half mastiibarren logs float in biers, await their final rites —plywood and profitiiithe wind tuneless without branches to pluckthe outsidea swing forwardout the windowand into somethingbreaking bough into somethingfinding medicinein pain and poison
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Matros, Olena M., Svitlana O. Mykhailovyna, Olga P. Ratushna, and Oleh M. Polishchuk. "Efficient Accounting for Individual Entrepreneurs: The Strategies and Prospects in the Modern Business Environment." Business Inform 4, no. 555 (2024): 163–70. http://dx.doi.org/10.32983/2222-4459-2024-4-163-170.
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This article examines the importance of proper financial accounting for individual entrepreneurs and highlights the need to adapt the legal and tax environment to the needs of this specific category of business. Accounting for individual entrepreneurship consists of accounting for income and accounting for goods, which include services provided or the sale of goods. The mandatory details of primary documents required for the correct accounting of individual entrepreneurs in accordance with the Law of Ukraine «On Accounting and Financial Reporting in Ukraine» are considered. The features of compiling and signing primary documents, which can be created both in paper and electronic form, are allocated. Additional details are also considered, which may be included depending on the nature of the business operation and the requirements of the law. In the context of modern technologies, an overview of the electronic exchange of documents and the requirements for electronic signatures for their recognition is also provided. The issue of insignificant deficiencies in documents containing information on business operations and their impact on the recognition of such operations is studied. Deficiencies that do not interfere with the identification of the participants in the operation and contain important details, such as the date of preparation, the name of the enterprise, etc., are not considered grounds for non-recognition of the operation. It is underlined that each situation will be checked individually by the supervisory authority, so it is recommended to carefully monitor the availability and accuracy of all details of the document. In particular, minor deficiencies are considered on the example of an invoice, pointing out those details, the absence of which is a minor drawback. The article examines in detail the accounting of incomes of individual entrepreneurs and identifies its important aspects. The authors focus on the fact that income accounting can be carried out in any form, but specify that individual entrepreneurs are subject to financial responsibility for failure to ensure the safekeeping of primary documents.
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Gavrila, Lucian-Ionut, and Alexandru Popa. "A novel algorithm for clearing financial obligations between companies - An application within the Romanian Ministry of economy." Algorithmic Finance 9, no. 1-2 (2021): 49–60. http://dx.doi.org/10.3233/af-200359.
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The concept of clearing or netting, as defined in the glossaries of European Central Bank, has a great impact on the economy of a country influencing the exchanges and the interactions between companies. On short, netting refers to an alternative to the usual way in which the companies make the payments to each other: it is an agreement in which each party sets off amounts it owes against amounts owed to it. Based on the amounts two or more parties owe between them, the payment is substituted by a direct settlement. In this paper we introduce a set of graph algorithms which provide optimal netting solutions for the scale of a country economy. The set of algorithms computes results in an efficient time and is tested on invoice data provided by the Romanian Ministry of Economy. Our results show that classical graph algorithms are still capable of solving very important modern problems.
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Voronin, Vladislav V. "JUDICIAL PRACTICE ON THE ADOPTION OF DECISION ON CONFORMITY OF VALUE ADDED TAXPAYER TO THE CRITERIA OF THE TAXPAYER’S RISK BY THE STATE TAX SERVICE BODIES OF UKRAINE." Bulletin of Alfred Nobel University Series "Law" 2, no. 3 (2021): 96–101. http://dx.doi.org/10.32342/2709-6408-2021-2-3-11.
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The article is devoted to the analysis of judicial practice on the recognition by the State Tax Service bodies of Ukraine (hereinafter - the State Tax Service of Ukraine) of the competence of the value added taxpayers to the risk criteria of taxpayers. This article analyzes the grounds for making decisions on compliance of value added taxpayers with the taxpayer’s risk criterion, analyzes such taxpayer risk criteria, provides analysis of decisions of the Supreme Court of Ukraine and lower courts concerning disputes on recognition of taxpayers’ compliance with risk criteria, the validity and legality of the legal position of the State Tax Service of Ukraine and the legality of such actions by regulatory authorities in terms of electronic document management and compliance with their defined procedure. The problematic issues that have arisen in the tax sphere in electronic document management are identified and solutions are proposed. It is determined that one of the problematic areas of value added tax administration is the adoption of decisions by regulatory authorities on compliance of taxpayers with risk criteria. The terminology is analyzed and it is determined that the value added tax (hereinafter - VAT) is an indirect tax, which is determined and levied in accordance with the provisions of the current Tax Code of Ukraine (hereinafter - the Tax Code of Ukraine). Therefore, VAT is a national indirect, ie one that is a component of prices for goods, works and services supplied and provided, and includes tax liabilities for goods and services supplied, tax credit for such goods (services) and obligations the payment of tax to the state budget. It is analyzed that the taxpayer is obliged to draw up a tax invoice and register in the Unified Register of Tax Invoices, have the necessary economic and industrial capabilities, staff, etc., to carry out business operations for the supply of goods, works or services declared activity. In addition, the laws of Ukraine establish clear grounds for suspending the registration of tax invoices, including compliance of the taxpayer with the risk criterion. Thus, the legislator has defined a clear list of conditions under which at least one of which, namely but not limited to registration on invalid documents, lack of open bank accounts, failure to report VAT and the availability of regulatory authorities information about the risk of business transactions. Such inclusion of the payer in the list of risk puts the company in a critical position and virtually terminates the activities of the payer and its counterparties in the tax chain, as all tax credit received from such a company is blocked, which creates additional tax burden on business.
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Jain, U., P. Mishra, A. Dash, and A. Pandey. "Multi-label multi-class text classification-enhanced attention in transformers with knowledge distillation." Journal of Applied Research and Technology 23, no. 1 (2025): 82–93. https://doi.org/10.22201/icat.24486736e.2025.23.1.2484.
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This scholarly paper introduces an innovative and comprehensive ideology that aims to significantly expand the utility of Named Entity Recognition (NER) through the application of Transformers in various Natural Language Processing (NLP) tasks. One prominent task that necessitates attention is the intricate classification of emails into multiple labels, wherein each label can be associated with not just one but potentially multiple independent classes. Despite the existence of several research methodologies attempting to address numerous challenges in this domain, the industry continues to face a substantial hurdle when it comes to accurately categorizing multi-label texts like financial emails, which can encompass diverse categories such as Payment Information, Invoice Information, Disputes, and more. Considering these challenges, our proposed methodology serves as a breakthrough solution, demonstrating remarkable performance in the classification task across a wide range of datasets, including Financial Emails and Consumer Complaint Datasets. By leveraging the power of advanced Transformers, we have achieved an exceptional accuracy rate of 94% for full match of the multi-label classes, while the accuracy for partial match to individual classes soared to an impressive 97%. This achievement not only highlights the effectiveness of the proposed approach but also showcases its potential to enhance the efficiency and reliability of NER applications in practical settings.
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Tian, Juanjuan, and Li Li. "Research on artificial intelligence of accounting information processing based on image processing." Mathematical Biosciences and Engineering 19, no. 8 (2022): 8411–25. http://dx.doi.org/10.3934/mbe.2022391.
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<abstract> <p>The rapid development and wide application of artificial intelligence is deeply affecting all aspects of human society. Combine artificial intelligence with the accounting industry, use computers to efficiently and automatically process accounting information, and let the accounting industry move towards the intelligent era. This can help people reduce the workload and speed up work efficiency. In recent years, with the rapid development of economy and technology, the use of financial instrument vouchers has exploded, but the processing requirements of financial instrument vouchers have become more and more efficient. Traditional accounting information processing methods, due to the staff's energy and ability, it is often difficult to quickly and accurately handle accounting information. This makes the processing of accounting information lack of timeliness, the degree of utilization of accounting information by enterprises is relatively low, and the demand for intelligent processing of accounting information is constantly pressing. In view of the above problems, this paper uses image processing technology to intelligently identify the content of accounting information to achieve automatic ticket input, improve work efficiency, reduce error rate and reduce labor costs. By simulating the actual 230 invoice images, the results show that the recognition accuracy rate is as high as 98.7%. The results show that the method is effective and has great application value, which is of great significance to the artificial intelligence of accounting information processing.</p> </abstract>
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Alexander, Dean, and I. Nyoman Pujawan. "PERANCANGAN SISTEM WAREHOUSE BERBASIS TEKNOLOGI OCR UNTUK MENINGKATKAN EFEKTIVITAS DAN EFISIENSI." Jurnal Ilmiah Teknik Industri 12, no. 1 (2024): 1–11. http://dx.doi.org/10.24912/jitiuntar.v12i1.28100.
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The rapid development of information technology is massive worldwide, racing to create solutions as a form of digital supply chain movement, massive use of information technology is also being carried out in the logistics business world which is part of supply chain activities, requiring accurate and fast information is a very important area to guarantee delivery of goods on time and on target. One important aspect of the logistics process is effective warehouse management. One of the main challenges in effective warehouse management is the process of receiving goods in the warehouse which still causes errors such as writing errors, inaccurate recording of the quantity, type and name of goods received and removed from the warehouse which takes quite a long time to process. There has been a lot of research carried out on data capturing activities with the application of OCR but not much has been found that focuses on managing printed and handwritten physical documents in the warehouse area, and preparation of data bases on websites connected to systematic OCR technology, and there is no analysis of technology investment. OCR regarding cost efficiency. Therefore, to help overcome this problem, this research carried out the design and implementation of a warehouse system based on OCR (Optical Character Recognition) technology with the name WareOCR and analyzed it from an investment perspective using the ROI method. From the research results obtained using the OCR creation model using the CNN method, the accuracy of the WareOCR system on test data is 93%, Return on Investment is calculated at 60.38%, in the NPV calculation in year 3 the figure is IDR 112,911,033 where NPV > 0 for the project implementing WareOCR it is acceptable that this investment provides added value of 493% of the total project cost. This research also has research limitations in the form of invoice formats which need to be standardized so they can be read with the WareOCR tool and the advantages of this research are that they can shorten the working time of the data entry warehouse in inputting information into the database.
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Cesarini, F., E. Francesconi, M. Gori, and G. Soda. "Analysis and understanding of multi-class invoices." International Journal on Document Analysis and Recognition 6, no. 2 (2003): 102–14. http://dx.doi.org/10.1007/s10032-002-0084-6.
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Larson, Jeffrey, and Francis Newman. "An implementation of scatter search to train neural networks for brain lesion recognition." Involve, a Journal of Mathematics 4, no. 3 (2011): 203–11. http://dx.doi.org/10.2140/involve.2011.4.203.
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Liu, Lanlan. "Construction of Financial Bill Recognition Model Based on Deep Learning." Journal of Combinatorial Mathematics and Combinatorial Computing 120, no. 1 (2024): 253–64. http://dx.doi.org/10.61091/jcmcc120-22.
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The common bills in life include VAT invoices, taxi invoices, train invoices, plane itineraries, machine-printed invoices, etc. Most of these common bills are presented in the form of fixed form templates, so template matching can be used. , for a certain fixed template bill, manually set the rules to determine the spatial position of the key area, extract the corresponding text information, or build a model with logical semantic relationship and spatial relative relationship between the bill texts of different attributes, from the global image of the image. Identify the required key text information in the text information. However, these methods are either limited by fixed ticket templates, or cannot guarantee considerable accuracy. The electronicization of paper invoices mainly needs to go through the steps of text detection, bill recognition and text recognition. Based on this, this paper adopts the DL method. Construct a financial bill recognition model and combine experiments to explore the effectiveness and superiority of the model. The results show that our model can achieve a recognition accuracy rate of up to 91\%, and also achieve a 26\% improvement in recognition speed.
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Ashkenazi, Sarit, Nitza Mark-Zigdon, and Avishai Henik. "Do subitizing deficits in developmental dyscalculia involve pattern recognition weakness?" Developmental Science 16, no. 1 (2012): 35–46. http://dx.doi.org/10.1111/j.1467-7687.2012.01190.x.
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Arizpe, Joseph, Danielle McKean, Jack Tsao, and Annie Chan. "Encoding and recognition of faces involve different eye-movement dynamics." Journal of Vision 17, no. 10 (2017): 1008. http://dx.doi.org/10.1167/17.10.1008.
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Bluthe´, Rose-Marie, and Robert Dantzer. "Social recognition does not involve vasopressinergic neurotransmission in female rats." Brain Research 535, no. 2 (1990): 301–4. http://dx.doi.org/10.1016/0006-8993(90)91613-l.
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Bauer, Daniel, Jakob Wegener, and Kaspar Bienefeld. "Recognition of mite-infested brood by honeybee (Apis mellifera) workers may involve thermal sensing." Journal of Thermal Biology 74 (May 2018): 311–16. http://dx.doi.org/10.1016/j.jtherbio.2018.04.012.
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SÁNCHEZ, LYDIA, and MANUEL CAMPOS. "Object recognition and content." Empedocles: European Journal for the Philosophy of Communication 2, no. 2 (2011): 207–26. http://dx.doi.org/10.1386/ejpc.2.2.207_1.
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Puzzles concerning attitude reports are at the origin of traditional theories of content. According to most of these theories, content has to involve some sort of conceptual entities, like senses, which determine reference. Conceptual views, however, have been challenged by direct reference theories and informational perspectives on content. In this paper we lay down the central elements of the more relevant strategies for solving cognitive puzzles. We then argue that the best solution available to those who maintain a view of content as truth conditions is to abandon the idea that content is the only element of mental attitudes that can make a difference as to the truth value of attitude reports. We finally resort to means of recognition of objects as one obvious element that helps explain differences in attitudes.
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Qu, Yanxu, Wei Gao, and Chao Liu. "SitPAA: Sitting Posture and Action Recognition Using Acoustic Sensing." Electronics 13, no. 1 (2023): 40. http://dx.doi.org/10.3390/electronics13010040.
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The technologies associated with recognizing human sitting posture and actions primarily involve computer vision, sensors, and radio frequency (RF) methods. These approaches often involve handling substantial amounts of data, pose privacy concerns, and necessitate additional hardware deployment. With the emergence of acoustic perception in recent times, acoustic schemes have demonstrated applicability in diverse scenarios, including action recognition, object recognition, and target tracking. In this paper, we introduce SitPAA, a sitting posture and action recognition method based on acoustic waves. Notably, our method utilizes only a single speaker and microphone on a smart device for signal transmission and reception. We have implemented multiple rounds of denoising on the received signal and introduced a new feature extraction technique. These extracted features are fed into static and dynamic-oriented networks to achieve precise classification of five distinct poses and four different actions. Additionally, we employ cross-domain recognition to enhance the universality of the classification results. Through extensive experimental validation, our method has demonstrated notable performance, achieving an average accuracy of 92.08% for posture recognition and 95.1% for action recognition. This underscores the effectiveness of our approach in providing robust and accurate results in the challenging domains of posture and action recognition.