PaddleOCR 3.5: Running OCR and Document Parsing Tasks with a Transformers Backend

HunyuanOCR-1.5: Making Lightweight OCR VLMs Faster and Better

We present HunyuanOCR-1. 5, a lightweight end-to-end OCR-specialized vision-language model.

Supercharge your OCR Pipelines with Open Models

DocOCR-Eval: A Correction-Based Framework for OCR Tool Selection Without Ground Truth

arXiv:2607. 16203v1 Announce Type: cross Abstract: Document parsing is a foundational step for document understanding tasks such as visual question answering and key information extraction, as it transforms unstructured scanned images into structured representations by extracting textual, visual, and layout information.

Finetuning olmOCR to be a faithful OCR-Engine

Parse Scanned PDFs for RAG with EasyOCR: Free OCR Gives You Words, Not a Document

Enterprise Document Intelligence [Vol. 1 #5quinquies] - Same 1974 scanned PDF, two engines.

Judge a Book by its Cover: Investigating Multi-Modal LLMs for Multi-Page Handwritten Document Transcription

arXiv:2502. 20295v3 Announce Type: replace-cross Abstract: Handwriting text recognition (HTR) remains a challenging task.

Introducing Mistral OCR 3

Introducing Mistral OCR 4

Mistral OCR 4 delivers enterprise document AI with 170-language support, bounding boxes, and self-hosted deployment.

ClinOCR-Bench: A Comprehensive Clinical Scanned Document Dataset for Optical Character Recognition Model Evaluation

arXiv:2607. 03650v1 Announce Type: cross Abstract: Extracting textual information from scanned medical documents, such as external laboratory reports and manually filled forms, has been a major challenge in modern electronic health records (EHRs).

OvisOCR2 Technical Report

arXiv:2607. 13639v1 Announce Type: cross Abstract: We introduce OvisOCR2, a 0.

Evaluating Structured Information Extraction with Open Models in a High Risk Public Sector Application

The paper evaluates open-source OCR, LLM, and VLM systems on a high‑risk public sector task: extracting structured data from student application documents. Results show that VLMs generally outperform OCR+LLM pipelines, yet only 4 of 35 configurations achieve F1 scores above 0.5, with most combinations scoring below 0.25. Model size and input quality, especially preserving OCR structure, are critical factors influencing performance.

A Benchmark of State-Space Models vs. Transformers and BiLSTM-based Models for Historical Newspaper OCR

arXiv:2604. 00725v2 Announce Type: replace-cross Abstract: End-to-end OCR for historical newspapers remains challenging, as models must handle long text sequences, degraded print quality, and complex layouts.