Amazon Nova 2 Lite and Anthropic's Claude Sonnet 4.6 have been paired to create a cost-effective solution for digitizing scanned documents. This two-model pipeline reduces costs by two-thirds compared to single-model alternatives, providing high confidence name-to-face associations from yearbook scans.
The new digitization solution utilizes a two-model pipeline: Amazon Nova 2 Lite and Claude Sonnet 4.6. Nova 2 Lite performs multimodal extraction in one API call, detecting images and text on scanned yearbook pages, while Claude applies spatial reasoning to appropriately associate names with faces.
This dual-model approach has proven to be approximately two-thirds cheaper per page compared to traditional methods using a single vision-language model. A detailed cost breakdown is provided in the original article, demonstrating the efficiency gained.
Testing on 336 digitized yearbook pages showed the model produced 3,122 name-to-face associations, achieving a confidence rate of 93% or higher. This high level of accuracy indicates reliability for large-scale document processing.
In the pipeline, Nova 2 Lite is configured to return both detected images and associated text with metadata in response to a Converse API call. Low reasoning settings were found sufficient for this extraction task, ensuring cost-effectiveness without compromising quality.
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Amazon Nova 2 Lite and Anthropic's Claude Sonnet 4.6 have been paired to create a cost-effective solution for digitizing scanned documents. This two-model pipeline reduces costs by two-thirds compared to single-model alternatives, providing high confidence name-to-face associations from yearbook scans.