What software turns complex pages into AI content?

Core explainer

What architectures enable reliable layout-aware extraction?

Layout-aware extraction is enabled by a transformer-based vision-encoder–decoder architecture that preserves layout, reading order, and content hierarchy while extracting text, tables, and other elements for AI tasks. This end-to-end approach leverages a heavy vision encoder and a lighter decoder to model complex document structures, enabling faithful reconstruction of how information is presented on a page.

The system combines a large, high-capacity encoder with a streamlined decoder to form a roughly 900‑million-parameter model. Adaptive compression reduces latent tokens from about 13,184 to 3,200, enabling efficient processing of long, multi-page documents without sacrificing fidelity. It uses unified tokenization that interleaves text tokens with spatial (bounding box) tokens and semantic (class) tokens, preserving the precise reading order and the content hierarchy across pages and columns.

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How does unified tokenization work with mixed content like text, boxes, and classes?

Unified tokenization interleaves textual tokens with spatial and semantic tokens to maintain the spatial relationships and reading order that define document structure. This design allows the model to treat text content, bounding-box coordinates, and element classes as a single, canonical sequence, rather than separate streams, which improves accuracy when reconstructing structured output.

By encoding coordinates alongside text in a unified stream, the approach can robustly identify layout features such as titles, headers, lists, captions, and tables, even in complex multi-column or multi-page layouts. The interleaved representation supports end-to-end processing where layout cues guide how content is prioritized, grouped, and extracted for downstream tasks like retrieval and comprehension.

This tokenization foundation underpins the system’s ability to produce outputs that align with downstream AI workflows, preserving the relationships between textual content and its visual context without requiring manual post-processing.

What are the outputs and formats for downstream AI tasks?

Outputs consist of structured text that includes bounding boxes and semantic class attributes for each extracted element, with formats that can be plain text or Markdown. This structure makes it straightforward for downstream models to reconstruct documents, search content, or feed content into LLM/VLM pipelines while keeping the original layout and reading order intact.

The structured outputs preserve content hierarchy, such as distinguishing titles, section headers, body text, lists, captions, and tables, enabling reliable downstream processing across multi-page documents and varying layouts. The presence of bounding-box coordinates and class labels allows downstream systems to reassemble the document layout when needed or to present content in a visually faithful way for human review or automated reasoning.

In enterprise settings, these outputs can be consumed by retrieval pipelines, content-indexing systems, and large-language/model workflows to support precise extraction, searchability, and context-aware understanding without extensive manual preprocessing.

How are data and benchmarks used to validate the approach?

Validation relies on large-scale pretraining on arXiv-5M, followed by fine-tuning on arXiv-5M plus human-annotated and public datasets to cover diverse layouts and content styles. This data strategy helps the model learn robust patterns for text, tables, and other document elements across domains.

Benchmarks described in the input include GOT Dense OCR Benchmark, PubTabNet, and RD-TableBench, with results such as near-perfect fidelity on GOT Dense OCR, PubTabNet achieving TEDS of 80.20 and S-TEDS of 92.20, and RD-TableBench showing a significant accuracy advantage over a popular competitor. These metrics support improvements in retrieval accuracy and structured content extraction for downstream LLM/VLM pipelines, while highlighting strengths and areas for further refinement in real-world documents.