Tracking models, datasets, and metrics for detecting tables in documents and parsing their internal structure.

Tracking the evolution of document layout detection: models, datasets, benchmarks, and metrics.

A reference page for all things Optical Character Recognition (OCR) using Large Language & Vision Models

We demonstrate how a specialized 25B parameter Mistral model, post-trained on domain-specific data, can outperform Google's Gemini 2.5 Flash by double-digit margins on insurance loss run extraction tasks.

Explore the critical challenge of uncertainty quantification in large language models. Learn about confidence estimation techniques, calibration metrics like ECE and MCE, and practical methods to improve model reliability from logit-based approaches to ensemble methods and post-hoc calibration.

A practical guide to reading and understanding research papers, with strategies for navigating academic literature.

Tracking benchmarks and datasets for table understanding, visual table QA, and LLM/VLM-era table evaluation. Scope: downstream reasoning and multimodal understanding over tables, not structural recognition.

A comprehensive survey of multimodal large language models from 2021 to 2024, covering encoder-only models, encoder-decoder architectures, decoder-only models, and specialized applications for documents and screens.

Tracking models, datasets, and methods for determining the logical reading sequence of detected document regions.

Tracking models and benchmarks for document understanding, visual information extraction, and document VQA.