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On three standard automation tasks, RPA beat an LLM computer-use agent on speed and reliability, while the agent cut development time from hours to minutes.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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In head-to-head tests on three standard automation challenges, RPA bots beat LLM computer-use agents on speed and reliability, while the agents needed far less development time.

T0 review reviewed 2026-08-05 challenge →

load-bearing objection Useful early empirical anchor, but the abstract oversells reliability and the P3 task versions may not be like-for-like. the 3 major comments →

arxiv 2509.04198 v1 pith:FSNDIJSQ submitted 2025-09-04 cs.CY cs.MA

Are LLM Agents the New RPA? A Comparative Study with RPA Across Enterprise Workflows

Petr Pr\r{u}cha , Michaela Matou\v{s}kov\'a , Jan Strnad This is my paper
classification cs.CY cs.MA
keywords Robotic Process AutomationLLM agentsAgentic Automation with Computer Useenterprise workflow automationempirical comparisonUiPathAnthropic Computer Use Agent
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper asks whether LLM-driven agents that operate a computer like a human user, called Agentic Automation with Computer Use (AACU), can replace traditional Robotic Process Automation (RPA) in enterprise workflows. The authors benchmarked one RPA platform against one public-beta computer-use agent on three standard RPA Challenge tasks: dynamic web-form data entry, stock-price monitoring with threshold alerts, and invoice OCR extraction. They found RPA significantly faster on the two fully measured processes and more successful (10/10 vs 9/10 and 6/10 successful runs), while the agent's prompt-based setup took 10–15 minutes versus 38–240 minutes of RPA scripting. The paper concludes that current AACU is not production-ready for stable repetitive work, but shows real promise for rapid prototyping, lightweight automation, and flexible handling of changing interfaces. The practical upshot is a division of labour: keep RPA for high-volume stable processes, watch AACU for quick, adaptive automation.

Core claim

On the paper's own terms, the central discovery is a quantified trade-off, not a single winner. In the two processes that were fully benchmarked, the RPA tool completed runs in 53.9 and 20 seconds on average with 10/10 successes, while the LLM computer-use agent took 109.8 and 202.8 seconds with 9/10 and 6/10 successes, and in a third process the agent could not finish the full task at all. Yet the agent achieved its first successful run after only 10–15 minutes of prompt writing, compared with roughly 38–240 minutes of RPA development, and it correctly recognized fields and OCR text even when page layouts shifted. The authors reject their hypothesis that AACU is faster, find no statisticall

What carries the argument

The load-bearing object is the controlled benchmark protocol: three standard RPA Challenge tasks (dynamic web-form data entry, threshold-based monitoring, invoice OCR extraction) run ten times per technology where possible, measuring execution time, success count, and time-to-first-successful-run. AACU is represented by a public-beta computer-use agent that receives screenshots and emits mouse and keyboard actions; RPA is represented by a commercial automation studio. Welch's t-test on run times, Fisher's exact test on success rates, and descriptive comparison of development times carry the quantitative argument.

Load-bearing premise

The invoice task was shortened to a single page for the LLM agent, and the paper does not state clearly whether the RPA baseline ran the same shortened version or the full challenge, so the P3 speed and reliability comparison may not be like-for-like.

What would settle it

Rerun P2 and P3 with byte-identical task definitions for both technologies (same pages, same row counts, same output format), and also run the full, unshortened invoice task with the agent using a larger context window. If the agent then matches or beats RPA on execution time and success rate, the paper's rejection of its first two hypotheses would be overturned.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • For stable, repetitive, high-volume workflows, RPA remains the stronger choice on current evidence; AACU is not yet a drop-in replacement.
  • For lightweight, infrequent automations, the agent's very low setup time can make automation economically viable where scripting effort previously blocked it.
  • AACU's demonstrated ability to read shifting form layouts and OCR content points to tasks where RPA's rigid selectors fail.
  • Hybrid designs, with an agent orchestrating and RPA executing critical steps, become an obvious next step for combining quick setup with reliable runs.
  • The paper's failure cases (session freezes, lost context on long tasks) make context length and state persistence the key levers for closing the reliability gap.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the measured gap is mostly beta-tool instability rather than a fundamental LLM limit, rerunning these benchmarks on newer computer-use models could shrink or reverse the speed and reliability differences.
  • The reported ~$0.28 cost per invoice run, combined with near-zero setup time, implies an economic crossover: for processes that run rarely, an agent may be cheaper end-to-end even though each execution is slower.
  • Because the invoice task was shortened to the first page only for the agent, the paper defines AACU's current viability as limited to small-context tasks; testing longer document sets would show whether the reliability gap grows with task length.
  • A direct test that holds the task definition byte-identical and deliberately changes page layouts would separate the agent's perceptual strengths from its orchestration weaknesses.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. This manuscript compares UiPath RPA with Anthropic's Computer Use Agent (referred to as AACU) on three standardized RPA challenge tasks: data entry (P1), stock market monitoring (P2), and invoice extraction (P3). For each process, the authors report execution time, success rate, and development time, and use Welch t-tests and Fisher exact tests to evaluate hypotheses H1 (speed), H2 (reliability), and H3 (development time). The results show that RPA is significantly faster on P2 and P3, that reliability differences are not statistically significant, and that AACU appears to require less development time. Important caveats are present: P1 has only a single AACU run, P3 was shortened for AACU, and development time is measured as a single observation per process. The paper concludes that RPA outperforms AACU in speed and reliability and that AACU offers significantly reduced development time and better flexibility.

Significance. If the results are fully supported, this paper would provide useful early empirical evidence on a topic largely driven by vendor claims. The use of well-known benchmark processes, a version-pinned agent environment, a public repository, and explicit discussion of limitations are genuine strengths. The main value is as a preliminary, field-experiment-style benchmark rather than a definitive evaluation. However, two load-bearing issues must be addressed: the abstract/conclusion overstate reliability despite non-significant Fisher tests, and the description of P3 leaves open the possibility that RPA and AACU ran tasks with different scope. After revision, including clarification or re-running, this could be an acceptable archival contribution.

major comments (3)
  1. [Sections 3 and 4, Process 3] The description of P3 is ambiguous about whether the UiPath baseline ran the same task as the AACU. Section 3 is headed 'RPA challenge Invoices – Simplified for AACU' and states 'we limited the scope of this challenge to the first page only, which contains four invoices.' Section 4 states P3 'was shortened to fit within the current context limitations of the agent.' The paper never states explicitly that the UiPath implementation was also restricted to the first page. Because P3 is one of only two processes with full 10-run measurements for both technologies, and it underlies the largest speed difference (20 s vs 202.8 s) and the only near-significant reliability comparison (p=0.0866), a task mismatch would confound the H1 and H2 conclusions. Please state explicitly that both technologies ran exactly the same scope. If they did not, rerun the comparison on a matched task or remove P3 fro
  2. [Section 5, H2; Abstract and Conclusion] The Fisher exact tests in Table 3 are p=1.0 for P2 and p=0.0866 for P3; the Results section correctly says 'we cannot confirm that the AACU is either better or worse than RPA in terms of reliability based on the current data.' However, the Abstract and Section 7 claim that 'RPA outperforms AACU in ... reliability' and 'RPA outperformed AACU in both speed and reliability.' This is an overstatement of non-significant evidence. The reliability conclusions should be rephrased to say that the observed success rates favored RPA but the differences were not statistically significant at the 0.05 level.
  3. [Section 5, Development Time Analysis; Conclusion] The claim that 'AACU significantly reduces development time' is not supported by the data. Table 4 reports one measurement per process, and P1 has no AACU development time. The authors themselves note that 'no statistical test could be applied to validate Hypothesis 3.' The word 'significantly' should be removed or replaced with 'descriptively' or 'in this limited sample.' This affects the Abstract, Section 5, and Section 7.
minor comments (5)
  1. [Table 1] The column headers contain typos: 'Succesful' and 'Unsuccesful' should be 'Successful' and 'Unsuccessful.' The word 'challange' appears in several places (e.g., Section 3 headings).
  2. [Section 5, Table 2] The p-value for P3 is reported as '0.0000118'; for consistency and readability, it should be expressed as '<0.0001' or '1.2e-5.' Also, the degrees of freedom for the Welch t-tests are not reported, which would help readers assess the critical values.
  3. [Section 5, H1] The text says 'leading to the rejection of the null hypothesis' after finding RPA is faster. The null hypothesis should be stated explicitly (e.g., equal mean execution times) to avoid ambiguity about what is being rejected.
  4. [References and Related Work] The citation style is inconsistent: some references are cited as author-year (e.g., Chakraborti et al., 2022) while others are numeric [10,11]. Please use one consistent style. Also, the statement in Section 2 that 'the term agentic automation does not currently yield any relevant literature focused specifically on business process automation' is contradicted by later citations such as ProAgent [12].
  5. [Appendix 1] The appendix contains a typo: 'invoice management systém' should be 'invoice management system.' Minor language editing throughout would improve readability.

Circularity Check

0 steps flagged

No circularity: the study is a direct empirical measurement comparison with no fitted parameters, derived quantities, or self-citation chains.

full rationale

This paper is an empirical benchmark study comparing RPA (UiPath) with an LLM computer-use agent (AACU) on three standardized tasks. The central claims—RPA is faster and more reliable, AACU requires less development time—are supported by directly measured outcomes (execution time, success/failure counts, development time). There are no fitted parameters, no statistical models whose predictions are compared to the data used to fit them, and no quantities that are defined in terms of the outcomes they purport to explain. The only model involvement is using Claude to rewrite its own prompt for Process 3; this is an input configuration choice, not a fitted parameter, and it does not define or predetermine the measured success metric. The paper contains no load-bearing self-citations: the references are to external prior work, industry documentation, and public challenges; none of the paper's conclusions are justified by citing the authors' own prior results. The significant concern noted in the manuscript—that Process 3 was 'Simplified for AACU' and shortened to the first page—is an experimental validity and fairness issue regarding whether RPA and AACU ran identical tasks, not a circularity issue. It concerns confounded comparison, not reduction of the conclusion to its inputs by definition. Therefore the appropriate circularity score is 0.

Axiom & Free-Parameter Ledger

0 free parameters · 4 axioms · 0 invented entities

The paper's conclusions rest on the representativeness of the benchmark tasks, the representativeness of the two chosen tools, the comparability of the (reduced) P3 task across conditions, and the validity of a single observed development time. No free parameters were fit to data and no new entities were postulated.

axioms (4)
  • domain assumption The three rpachallenge.com tasks are representative of typical enterprise RPA workflows.
    Section 3 states the processes 'are representative of typical RPA practices', but they are toy web challenges, not enterprise systems.
  • domain assumption The chosen versions (UiPath 2023.4.0, claude-sonnet-4-20250514) represent their technology classes.
    Section 4 tests one RPA product and one agent product; 'no alternative agentic automation tools were accessible' (Section 6).
  • domain assumption The P3 task, shortened to the first invoice page, is comparable across technologies.
    Section 3 says the challenge 'was shortened to fit within the current context limitations of the agent'; it is not stated whether RPA ran the same reduced version.
  • domain assumption A single successful run defines development time and is a meaningful proxy for development effort.
    Section 4: development time is time until the first successful run, measured once per process with a stopwatch; no error bars.

reviewed 2026-08-05 · how reviews work

0 comments
Cite this review

Pith. "Pith review of Are LLM Agents the New RPA? A Comparative Study with RPA Across Enterprise Workflows." pith.science (2026). https://pith.science/paper/FSNDIJSQ

@misc{pith2026250904198,
  author       = {Pith},
  title        = {Pith review of: Are LLM Agents the New RPA? A Comparative Study with RPA Across Enterprise Workflows},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/FSNDIJSQ}},
  note         = {Machine review of arXiv:2509.04198}
}
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read the original abstract

The emergence of large language models (LLMs) has introduced a new paradigm in automation: LLM agents or Agentic Automation with Computer Use (AACU). Unlike traditional Robotic Process Automation (RPA), which relies on rule-based workflows and scripting, AACU enables intelligent agents to perform tasks through natural language instructions and autonomous interaction with user interfaces. This study investigates whether AACU can serve as a viable alternative to RPA in enterprise workflow automation. We conducted controlled experiments across three standard RPA challenges data entry, monitoring, and document extraction comparing RPA (via UiPath) and AACU (via Anthropic's Computer Use Agent) in terms of speed, reliability, and development effort. Results indicate that RPA outperforms AACU in execution speed and reliability, particularly in repetitive, stable environments. However, AACU significantly reduces development time and adapts more flexibly to dynamic interfaces. While current AACU implementations are not yet production-ready, their promise in rapid prototyping and lightweight automation is evident. Future research should explore multi-agent orchestration, hybrid RPA-AACU architectures, and more robust evaluation across industries and platforms.

Figures

Figures reproduced from arXiv: 2509.04198 by Jan Strnad, Michaela Matou\v{s}kov\'a, Petr Pr\r{u}cha.

Figure 1
Figure 1. Figure 1: Visualisation of process 1 the RPA challange [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: Visualisation of process 3 the RPA invoice challange The RPA bot or AACU navigates to https://rpachallengeocr.azurewebsites.net, where it interacts with a table of invoice records. From this table, the agent must extract the Invoice ID and Due Date for each entry. Additionally, each row contains a link to view the invoice as an image. From the image, the agent must further extract the Invoice Number, Invoi… view at source ↗

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Forward citations

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Reference graph

Works this paper leans on

28 extracted references · 16 canonical work pages · cited by 1 Pith paper · 1 internal anchor

  1. [1]

    In: Marrella, A., Matulevicius, R., Gabryelczyk, R., Axmann, B., Vuksic, V.B., Gaaloul, W., Stemberger, M.I., Ko, A., and Lu, Q

    Chakraborti, T., Rizk, Y., Isahagian, V., Aksar, B., Fuggitti, F.: From Natural Lan- guage to Workflows: Towards Emergent Intelligence in Robotic Process Automa- tion. In: Marrella, A., Matulevicius, R., Gabryelczyk, R., Axmann, B., Vuksic, V.B., Gaaloul, W., Stemberger, M.I., Ko, A., and Lu, Q. (eds.) Business Process Management: Blockchain, Robotic Proc...

    doi:10.1007/9 2022

  2. [2]

    PLoS ONE

    Jansen, J.A., Manukyan, A., Al Khoury, N., Akalin, A.: Leveraging large language models for data analysis automation. PLoS ONE. 20, e0317084 (2025). https://doi.org/10.1371/journal.pone.0317084

    doi:10.1371/journal.pone.0317084 2025

  3. [3]

    Towards Intelligent Robotic Process Automation for BPMers

    Agostinelli, S., Marrella, A., Mecella, M.: Towards Intelligent Robotic Process Automation for BPMers, https://arxiv.org/abs/2001.00804, (2020). https://doi.org/10.48550/ARXIV.2001.00804

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2001.00804 2001

  4. [4]

    Enterprise Information Systems

    Wewerka, J., Reichert, M.: Robotic process automation - a systematic mapping study and classification framework. Enterprise Information Systems. 17, 1986862 (2023). https://doi.org/10.1080/17517575.2021.1986862

    arXiv 2023

  5. [5]

    IEEE Access

    Enriquez, J.G., Jimenez-Ramirez, A., Dominguez-Mayo, F.J., Garcia-Garcia, J.A.: Robotic Process Automation: A Scientific and Industrial Systematic Mapping Study. IEEE Access. 8, 39113 –39129 (2020). https://doi.org/10.1109/ACCESS.2020.2974934

    work page arXiv 2020

  6. [6]

    Syed, R., Suriadi, S., Adams, M., Bandara, W., Leemans, S.J.J., Ouyang, C., ter Hofstede, A.H.M., van de Weerd, I., Wynn, M.T., Reijers, H.A.: Robotic Process 14 P. Průcha. M. Matoušková and J. Strnad Automation: Contemporary themes and challenges. Computers in Industry. 115, 103162 (2020). https://doi.org/10.1016/j.compind.2019.103162

    work page arXiv 2020

  7. [7]

    Journal of Computer Information Systems

    Hughes, L., Dwivedi, Y.K., Malik, T., Shawosh, M., Albashrawi, M.A., Jeon, I., Dutot, V., Appanderanda, M., Crick, T., De’, R., Fenwick, M., Gunaratnege, S.M., Jurcys, P., Kar, A.K., Kshetri, N., Li, K., Mutasa, S., Samothrakis, S., Wade, M., Walton, P.: AI Agents and Agentic Systems: A Multi-Expert Analysis. Journal of Computer Information Systems. 1 –29...

    arXiv 2025

  8. [8]

    Arthroscopy: The Journal of Arthroscopic & Related Surgery

    Woo, J.J., Yang, A.J., Olsen, R.J., Hasan, S.S., Nawabi, D.H., Nwachukwu, B.U., Williams, R.J., Ramkumar, P.N.: Custom Large Language Models Improve Ac- curacy: Comparing Retrieval Augmented Generation and Artificial Intelligence Agents to Noncustom Model s for Evidence -Based Medicine. Arthroscopy: The Journal of Arthroscopic & Related Surgery. 41, 565 -...

    work page doi:10.1016/j.arthro.2024.10.042 2025

  9. [9]

    Nunes, I., V

    Martins, A., Londral, A., L. Nunes, I., V. Lapão, L.: Unlocking human -like con- versations: Scoping review of automation techniques for personalized healthcare interventions using conversational agents. International Journal of Medical Infor- matics. 185, 105385 (2024). https://doi.org/10.1016/j.ijmedinf.2024.105385

    arXiv 2024

  10. [10]

    Gaurav Samdani, Kabita Paul, Flavia Saldanha: Agentic AI in the Age of Hyper - Automation. World J. Adv. Eng. Technol. Sci. 8, 416 –427 (2023). https://doi.org/10.30574/wjaets.2023.8.1.0042

    work page doi:10.30574/wjaets.2023.8.1.0042 2023

  11. [11]

    Agentic AI: A Concep- tual Taxonomy, Applications and Challenges, https://arxiv.org/abs/2505.10468, (2025)

    Sapkota, R., Roumeliotis, K.I., Karkee, M.: AI Agents vs. Agentic AI: A Concep- tual Taxonomy, Applications and Challenges, https://arxiv.org/abs/2505.10468, (2025). https://doi.org/10.48550/ARXIV.2505.10468

    doi:10.48550/arxiv.2505.10468 2025

  12. [12]

    https://doi.org/10.48550/ARXIV.2311.10751

    Ye, Y., Cong, X., Tian, S., Cao, J., Wang, H., Qin, Y., Lu, Y., Yu, H., Wang, H., Lin, Y., Liu, Z., Sun, M.: ProAgent: From Robotic Process Automation to Agentic Process Automation, https://arxiv.org/abs/2311.10751, (2023). https://doi.org/10.48550/ARXIV.2311.10751

    Pith/arXiv arXiv doi:10.48550/arxiv.2311.10751 2023

  13. [13]

    Lamanna, C.: Announcing new computer use in Microsoft Copilot Studio for UI automation, https://www.microsoft.com/en -us/microsoft-copilot/blog/copilot-stu- dio/announcing-computer-use-microsoft-copilot-studio-ui-automation/

    work page

  14. [14]

    Xiong: Invoice Extraction with OCR, https://github.com/MaxineXiong/Invoice - Extraction-OCR-Challenge-RPA

    work page

  15. [15]

    RPA Challenge Invoice Extraction in UiPath | Automation OCR Challenge | Ui- Path RPA. (2020)

    work page 2020

  16. [16]

    London School of Economics and Political Science, LSE Library (2015)

    Willcocks, L.P., Lacity, M., Craig, A.: The IT function and robotic process auto- mation. London School of Economics and Political Science, LSE Library (2015)

    work page 2015

  17. [17]

    In: Grefen, P

    Syed, R., Wynn, M.T.: Robotic process automation: a review of the state -of-the- art. In: Grefen, P. and Vanderfeesten, I. (eds.) Handbook on Business Process Ma- nagement and Digital Transformation. pp. 333 –362. Edward Elgar Publishing (2024). https://doi.org/10.4337/9781802206098.00024

    work page arXiv 2024

  18. [18]

    In: 2024 47th MIPRO ICT and Electronics Conven- tion (MIPRO)

    Perković, G., Drobnjak, A., Botički, I.: Hallucinations in LLMs: Understanding and Addressing Challenges. In: 2024 47th MIPRO ICT and Electronics Conven- tion (MIPRO). pp. 2084 –2088. IEEE, Opatija, Croatia (2024). https://doi.org/10.1109/MIPRO60963.2024.10569238. Are LLM Agents the New RPA? 15

    arXiv 2024

  19. [19]

    In: 2025 IEEE Con- ference on Secure and Trustworthy Machine Learning (SaTML)

    Chao, P., Robey, A., Dobriban, E., Hassani, H., Pappas, G.J., Wong, E.: Jailbrea- king Black Box Large Language Models in Twenty Queries. In: 2025 IEEE Con- ference on Secure and Trustworthy Machine Learning (SaTML). pp. 23–42. IEEE, Copenhagen, Denmark (2 025). https://doi.org/10.1109/Sa- TML64287.2025.00010

    work page arXiv 2025

  20. [20]

    In: Pesquita, C., Skaf -Molli, H., Efthymiou, V., Kirrane, S., Ngonga, A., Collarana, D., Cerqueira, R., Alam, M., Trojahn, C., and Hertling, S

    Martino, A., Iannelli, M., Truong, C.: Knowledge Injection to Counter Large Lan- guage Model (LLM) Hallucination. In: Pesquita, C., Skaf -Molli, H., Efthymiou, V., Kirrane, S., Ngonga, A., Collarana, D., Cerqueira, R., Alam, M., Trojahn, C., and Hertling, S. (eds.) The Semantic Web: ESWC 2023 Satellite Events. pp. 182–

    work page 2023

  21. [21]

    https://doi.org/10.48550/ARXIV.2402.02047

    Spiess, C., Gros, D., Pai, K.S., Pradel, M., Rabin, M.R.I., Alipour, A., Jha, S., Devanbu, P., Ahmed, T.: Calibration and Correctness of Language Models for Code, https://arxiv.org/abs/2402.02047, (2024). https://doi.org/10.48550/ARXIV.2402.02047

    Pith/arXiv arXiv doi:10.48550/arxiv.2402.02047 2024

  22. [22]

    https://doi.org/10.48550/ARXIV.2308.05391

    Schwartz, S., Yaeli, A., Shlomov, S.: Enhancing Trust in LLM-Based AI Automa- tion Agents: New Considerations and Future Challenges, https://ar- xiv.org/abs/2308.05391, (2023). https://doi.org/10.48550/ARXIV.2308.05391

    Pith/arXiv arXiv doi:10.48550/arxiv.2308.05391 2023

  23. [23]

    Bus Inf Syst Eng

    van der Aalst, W.M.P., Bichler, M., Heinzl, A.: Robotic Process Automation. Bus Inf Syst Eng. 60, 269–272 (2018). https://doi.org/10.1007/s12599-018-0542-4

    doi:10.1007/s12599-018-0542-4 2018

  24. [24]

    eds: Robotic process automation: management, techno- logy, applications

    Czarnecki, C., Fettke, P. eds: Robotic process automation: management, techno- logy, applications. De Gruyter Oldenbourg, Berlin ; Boston (2021). Appendix 1 INVOICE DATA EXTRACTION TASK ENVIRONMENT: - Web browser (Firefox) with invoice management systém - Text editor (gedit) with example.csv file open OBJECTIVE: Extract and compile invoice information int...

    work page 2021

  25. [26]

    Click invoice download button for each ID

    work page

  26. [27]

    Open the downloaded invoice document

    work page

  27. [28]

    Extract the following fields: • Invoice Number • Invoice Date (DD-MM-YYYY format) • Company Name 16 P. Průcha. M. Matoušková and J. Strnad • Total Amount Due OUTPUT FORMAT (CSV): ID,DueDate,InvoiceNo,InvoiceDate,CompanyName,TotalDue SAMPLE DATA: 5jef1y8yx4t8yupbpo3fzg,25 -02-2019,10021,13-02-2019,Sit Amet Corp.,1234.40 g11rzk7loegnitbclwnh8k,06 -04-2019,2...

    work page 2019

  28. [185]

    https://doi.org/10.1007/978 -3- 031-43458-7_34

    Springer Nature Switzerland, Cham (2023). https://doi.org/10.1007/978 -3- 031-43458-7_34

    doi:10.1007/978 2023

This paper was first reviewed by deepseek-v4-flash on August 5, 2026.