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Testing Claude 3.5 Sonnet for document text extraction - Graphlit

Aug 07, 2024 - news.bensbites.com
The article discusses different approaches for text extraction from documents, with a focus on the use of Large Multimodal Models (LMMs) like OpenAI GPT-4o and Anthropic Claude 3.5 Sonnet. The author highlights the potential cost benefits of using multimodal models over existing cloud services like Azure AI Document Intelligence and Amazon Textract. The article also presents a case study where a customized prompt was used to instruct the model on how to analyze an image and return a formatted JSON response, with the JSON schema based on the internal mezzanine JSON format used by Graphlit.

The author notes that while Sonnet 3.5 had some issues with the accuracy of radio button extraction, it performed better than OpenAI GPT-4o in terms of accurately representing the document structure. The article concludes with the author's intention to continue investigating the use of multimodal models for text extraction and potentially incorporating them into the Graphlit preparation workflow.

Key takeaways:

  • There are several methods for text extraction from documents, including OCR and Large Multimodal Models (LMMs) like OpenAI GPT-4o and Anthropic Claude 3.5 Sonnet.
  • These models can provide a cost benefit for text extraction compared to existing cloud services like Azure AI Document Intelligence and Amazon Textract.
  • Anthropic Claude 3.5 Sonnet shows potential in document text extraction, though it has some issues with the accuracy of radio button extraction.
  • The team will continue to investigate the use of multimodal models for text extraction and consider adding them to the Graphlit preparation workflow.
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