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Efficient Information Extraction from Visually Rich Documents
In business workflows, visually rich documents (VRDs) like invoices, utility bills, and insurance quotes often present similar information in varying layouts and formats. Extracting data from these documents can significantly reduce manual effort.
Challenges and Solutions
Extracting information from VRDs poses challenges due to the need to understand both textual and visual properties of the documents. Many existing methods rely on supervised learning, which requires labor-intensive labeling of samples.
Pre-training strategies have been proposed to address this challenge, but they often require significant time and computational resources. In response, a team of researchers from Google AI proposed a Noise-Aware Training method (NAT) to train robust extractors with limited human-labeled samples within a bounded time.
Practical Value
The NAT method operates in three phases, leveraging labeled and unlabeled data to iteratively improve the performance of the extractor while respecting time constraints. This approach holds the potential to significantly improve the efficiency and scalability of document processing workflows in enterprise environments, ultimately enhancing productivity and reducing operational costs.
AI Solutions for Business
AI can redefine work processes by automating customer engagement and managing interactions across all customer journey stages. Implementing AI solutions gradually, starting with a pilot and expanding usage judiciously, can lead to measurable impacts on business outcomes.
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List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- Google AI Researchers Propose a Noise-Aware Training Method (NAT) for Layout-Aware Language Models
- MarkTechPost
- Twitter – @itinaicom
