document-parsing

LlamaIndex introduces a new LlamaParse feature that automatically extracts complex form fields into structured JSON without requiring a predefined schema, simplifying document form processing.

PaDoc introduces a layout-grounded parallel decoding method for end-to-end document parsers, decoupling layout and content decoding to reduce decoding depth and improve throughput. It achieves state-of-the-art results on OmniDocBenchFull and significantly speeds up inference compared to sequential baselines.

A developer benchmarks three PDF-parsing models (MinerU, Granite-Docling, PaddleOCR-VL) across six document types and twelve capabilities, finding MinerU drops footers unless its markdown is rebuilt while Granite-Docling outputs cleaner native markdown tables.

LlamaIndex introduces Parse Gateway, a page-level document OCR router that estimates each page's complexity and routes it to the appropriate parsing tier (LiteParse or LlamaParse), balancing cost, latency, and quality.

LiteParse is a fast, lightweight, open-source PDF parsing tool written in Rust, supporting image-to-PDF conversion natively and providing spatial text parsing with bounding boxes, available via multiple packages (Rust, Node.js, Python, WASM).

Jerry Liu announces a live webinar on parsing complex financial documents using LlamaIndex to enable cleaner structured outputs for agentic workflows.

LlamaParse now supports multi-layered bounding boxes (region, line, word) for granular document text attribution, improving auditability in invoices, research reports, and more.

DocOCR-Eval proposes an annotation-free framework that uses a correction and ranking strategy to evaluate and select OCR tools without ground truth labels, showing that aggregating multiple multimodal large language models improves alignment with human rankings.

HPD-Parsing introduces a hierarchical parallel decoding paradigm for VLM-based document parsing, replacing full-page autoregressive generation to achieve 4,752 tokens per second throughput (2.62x faster than prior models) while maintaining competitive accuracy.

OvisOCR2 is a 0.8B end-to-end document-parsing VLM that tops the OmniDocBench leaderboard, outperforming pipeline OCR systems on real-world scanned medical documents with higher accuracy and efficiency.

OvisOCR2 is a 0.8B parameter end-to-end document parsing model that converts document page images to Markdown, achieving state-of-the-art scores on public benchmarks through a combination of supervised fine-tuning, reinforcement learning, and model fusion.

OvisOCR2 is a new 0.8B end-to-end OCR model based on Qwen3.5-0.8B that converts full document pages directly into structured Markdown, including text, tables, formulas, and reading order. It achieves strong benchmark scores and is released under Apache 2.0 with vLLM support.

OvisOCR2 is a compact 0.8B end-to-end model for page-level document parsing, achieving state-of-the-art performance on OmniDocBench and PureDocBench benchmarks.

The Infinity-Parser2 technical report presents a large multimodal model for end-to-end document parsing, featuring a scalable data synthesis pipeline and multi-task reinforcement learning. It achieves state-of-the-art results on multiple benchmarks while releasing open-source model variants and a 5-million-sample bilingual corpus.

LlamaIndex announces improved routing in LlamaParse that selects lightweight parsing for simple text-heavy PDFs and heavier VLM-based parsing for complex pages with tables or charts, optimizing cost and accuracy.

Jerry Liu presents a framework for document parsing across accuracy, cost, and latency tradeoffs, introducing LiteParse as an open-source, low-latency parsing tool for AI agent loops, along with LlamaParse for high-accuracy modes.

LiteParse is a fast document parsing tool that runs locally, achieving ~3ms per page by skipping heavy AI and cloud overhead. It uses deterministic layout heuristics and selective OCR to output structured Markdown, making it ideal for real-time RAG pipelines and coding agents.

China open-sourced Unlimited OCR, an OCR model based on DeepSeek OCR that can transcribe entire books in a single pass with constant memory usage, achieving 93% on standard parsing benchmarks.

Baidu open-sources Unlimited OCR, a 3B parameter model (500M activated) that reads long documents in a single pass using Reference Sliding Window Attention (R-SWA), achieving state-of-the-art results on OmniDocBench.

Baidu has open-sourced the visual language model Unlimited-OCR, upgraded from DeepSeek-OCR, supporting one-shot parsing of extremely long documents, offering two inference modes: gundam (dense text in a single image) and base (multi-page/PDF).