DISCO: Document Intelligence Suite for COmparative Evaluation
📰 ArXiv cs.AI
DISCO is a document intelligence suite for comparative evaluation of OCR pipelines and vision-language models
Action Steps
- Evaluate OCR pipelines using DISCO
- Compare vision-language models (VLMs) on parsing and question answering tasks
- Assess performance across diverse document types, including handwritten text and multilingual scripts
- Analyze results to identify areas for improvement in document intelligence
Who Needs to Know This
AI engineers and researchers on a team benefit from DISCO as it enables them to evaluate and compare the performance of different OCR pipelines and VLMs, while data scientists can utilize it to improve document intelligence tasks
Key Insight
💡 DISCO enables comparative evaluation of OCR pipelines and VLMs to improve document intelligence tasks
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📄 Introducing DISCO: a document intelligence suite for comparative evaluation of OCR pipelines and VLMs 💡
Key Takeaways
DISCO is a document intelligence suite for comparative evaluation of OCR pipelines and vision-language models
Full Article
Title: DISCO: Document Intelligence Suite for COmparative Evaluation
Abstract:
arXiv:2603.23511v1 Announce Type: cross Abstract: Document intelligence requires accurate text extraction and reliable reasoning over document content. We introduce \textbf{DISCO}, a \emph{Document Intelligence Suite for COmparative Evaluation}, that evaluates optical character recognition (OCR) pipelines and vision-language models (VLMs) separately on parsing and question answering across diverse document types, including handwritten text, multilingual scripts, medical forms, infographics, and
Abstract:
arXiv:2603.23511v1 Announce Type: cross Abstract: Document intelligence requires accurate text extraction and reliable reasoning over document content. We introduce \textbf{DISCO}, a \emph{Document Intelligence Suite for COmparative Evaluation}, that evaluates optical character recognition (OCR) pipelines and vision-language models (VLMs) separately on parsing and question answering across diverse document types, including handwritten text, multilingual scripts, medical forms, infographics, and
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