AI BriefingDocument AI Key Tasks and Model Guide
·2022.11.21 09:00
Key point
A technical guide summarizing the key tasks of Document AI for document understanding and how to leverage multimodal models.
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Details
This covers the core technologies and use cases of Document AI for converting unstructured data within documents into digital workflows.
Key Use Cases and Technologies:
- OCR (Optical Character Recognition): An essential step for extracting text from images, with EasyOCR, PaddleOCR, and TrOCR as representative examples.
- Document Image Classification: Determines the type of document (invoice, report, etc.). Multimodal models such as LayoutLMv3 and Donut, which learn visual structure together, provide much higher accuracy than text-only approaches.
- Document Layout Analysis: Identifies individual components by understanding the physical structure of a document.
Key Insight: Multimodal Transformer models that combine text and visual layout information maximize performance compared to models that rely solely on text information. On the RVL-CDIP benchmark, text-based models showed 89% accuracy, while multimodal models achieved 95% accuracy.
