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Document AI

Document AI

2
posts
2026

H2O Document AI is a system designed to extract information from various document formats using a combination of AI technologies. It encompasses optical character recognition (OCR), intelligent character recognition (ICR), and natural language processing (NLP) to identify and extract key data points such as entity names, logos, and invoice numbers. The system supports automated data labeling, model training, and deployment via REST APIs, integrating with existing workflows and H2O MLOps for monitoring. This includes the development and release of multimodal foundation models like H2OVL Mississippi, which enhance OCR and document understanding capabilities by combining vision and language processing. These models, such as H2OVL Mississippi-0.8B and 2B, are optimized for efficiency and performance, outperforming larger models on specific benchmarks and offering advanced features like dynamic resolution and multi-scale adaptive cropping.

2026

Document AI

6/24/2026

This post introduces H2O Document AI, detailing its capabilities in extracting data from documents. It highlights the use of OCR, ICR, and NLP for information extraction, automated data labeling, and model training. The system's architecture involves ingestion, labeling, training, deployment (to H2O MLOps or custom environments), and consumption phases. It emphasizes seamless integration via REST APIs and its application for data scientists and business users to automate document processing tasks.

H2OVL Mississippi

6/24/2026

Introduces H2OVL Mississippi-2B and 0.8B, new multimodal foundation models specifically for OCR and Document AI use cases. Details their architecture, inspired by LLaVA and InternVL, using a ViT-MLP-LLM setup with dynamic resolution and multi-scale adaptive cropping. Explains the two-stage training methodology, including pretraining on large datasets for image-text alignment and fine-tuning with specific tasks like QA, OCR, reasoning, and captioning. Highlights performance benchmarks showing superiority in text recognition and competitive performance in image benchmarks.