Invoice data Extraction Using LLM and OCR

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.

Introduction

Efficient and accurate invoice processing is critical for businesses, especially those dealing with large volumes of financial documents. Manual processing is laborious, error-prone, and slow, leading to delays and higher costs. Advances in technology now enable automated invoice data extraction by combining Optical Character Recognition (OCR) and Large Language Models (LLMs). OCR converts scanned invoices into text, while LLMs improve data interpretation, validation, and contextual understanding, enhancing accuracy and efficiency.

The system architecture integrates OCR for text extraction and LLMs for analyzing and validating invoice data, with components for user interaction, data storage, security, and ERP integration. Methodologies include image preprocessing, OCR application, named entity recognition, field mapping, and rule-based validation to ensure data quality.

Performance is evaluated on accuracy, speed, scalability, robustness, user experience, and cost efficiency. Results show improvements in accuracy, processing speed, cost savings, regulatory compliance, scalability, and data-driven decision-making.

Conclusion

The Invoice Data Extraction Using OCR and LLM project successfully demonstrates an automated approach to extracting structured data from invoices, reducing manual effort and increasing accuracy. By combining OCR for initial text recognition and LLM models for context-aware field extraction, the system efficiently processes varied invoice formats, capturing key information like invoice number, date, and total amount. This solution not only streamlines accounting and record-keeping processes but also provides a scalable, adaptable framework for future needs. The integration of these advanced technologies enables businesses to enhance operational efficiency and data accessibility in real-time.

References

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Copyright

Copyright © 2025 Lata Gawade, Sarvadnya Sonawane, Sarah Sayyed, Meshka Dhumal, Rupali Wagh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.