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Document AI

2 posts · Jun 2026

What this trail covers

H2OVL Mississippi is a new multimodal foundation model family for OCR and Document AI use cases. H2OVL Mississippi-0.8B and 2B models offer strong performance in text recognition and image understanding, with the 0.8B model outperforming larger models on OCR benchmarks. The architecture is inspired by LLaVA and InternVL, utilizing a ViT-MLP-LLM setup with dynamic resolution and multi-scale adaptive cropping. Training involves a two-stage methodology with pretraining and fine-tuning stages tailored for document understanding. The Document AI platform integrates OCR, NLP, and deep learning to extract information from forms and unstructured text, enabling automated data labeling, intelligent information extraction, and seamless integration with existing workflows via REST APIs. Models can be deployed to H2O MLOps for monitoring and management, or to custom environments. The platform supports ingestion, labeling, training, deployment, and consumption of document data, transforming business operations by automating repetitive tasks and freeing up human resources for higher-value work. It caters to Data Scientists by simplifying the delivery of document processing pipelines and to Business Users by automating lower-level tasks and highlighting relevant information.

All H2O.ai trails
  1. Document AIThis post introduces H2OVL Mississippi, a new multimodal foundation model family for OCR and Document AI. It details the model's architecture, inspired by LLaVA and InternVL, featuring a ViT-MLP-LLM setup with dynamic resolution and multi-scale adaptive cropping. The training methodology involves a two-stage process of pretraining and fine-tuning specifically for document understanding. The post also outlines the H2O Document AI platform's capabilities, including OCR, NLP, and deep learning for data extraction, automated labeling, and integration via REST APIs. It describes the five-step workflow: Ingest, Label, Train, Deploy, and Consume, highlighting features like page orientation resolution, deskewing, text formatting optimization, and color binarization during ingestion. The integration with H2O MLOps for model management and deployment options are also mentioned.
  2. H2OVL MississippiIntroduces H2OVL Mississippi, a new family of multimodal foundation models (0.8B and 2B parameters) specifically for OCR and Document AI. Details their performance on OCRBench and other benchmarks, outperforming larger models. Describes the model architecture (ViT-MLP-LLM, dynamic resolution, MSAC) and the two-stage training methodology (pretraining on diverse datasets, fine-tuning for QA, OCR, reasoning, and chart/figure/table understanding). Provides examples of model capabilities.