How Intelligent Document Processing Enhances RPA Capabilities

As business needs expand and become more complex, traditional document processing has shown several shortcomings in RPA applications:

• Data accuracy issues: Traditional RPA systems rely on fixed rules to extract PDF data. When there are slight changes in the data format or content, errors may occur, which in turn affects the data quality and the effectiveness of the automated process.
• Document complexity challenge: As customer needs diversify, existing PDF solutions often find it difficult to accurately handle complex tables, charts, and formulas, resulting in inaccurate information extraction.
• Processing performance bottleneck: When processing large-scale documents, the speed often fails to meet expectations, thus affecting the overall efficiency.

Intelligent document processing and the integrated functions of RPA systems

• Convert images to PDF: Use OCR technology and intelligent image layout analysis and element recognition functions to accurately extract image information and export data. This function supports obtaining data from images according to process needs.

• PDF to Image: Meet the requirements for image clarity in different application scenarios. Users can adjust the output image size by customizing DPI parameters, improve the flexibility of document processing and display, and ensure cross-platform consistency and a high-quality viewing experience.

• PDF text extraction: Accurately and automatically extract coordinate-based information from PDF and automatically fill in the ERP system. With the support of AI technology, PDF table extraction becomes more accurate.

Export PDF annotations to XML: Use C#'s List data structure interface to directly return annotations and coordinate information. This direct data transmission method improves the efficiency and flexibility of RPA automation processing, simplifies the data management process, and reduces the complexity of document management.

Original link: RPA & IDP