Introducing Mistral OCR 3

Mistral OCR

Introducing Mistral OCR 4

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

Finetuning olmOCR to be a faithful OCR-Engine

SOTA OCR with Core ML and dots.ocr

Introducing Mistral 3

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).

Supercharge your OCR Pipelines with Open Models

PP-OCRv6 on Hugging Face: 50-Language OCR from 1.5M to 34.5M Parameters

Introducing Mistral Code

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

TongGuOCR: A Layout-Aware and Token-Augmented OCR Framework for Chinese Historical Documents

arXiv:2608. 07917v1 Announce Type: new Abstract: Chinese historical documents preserve valuable cultural heritage, but many collections remain accessible only as scanned page images, preventing full-text retrieval, collation, and computational analysis.

Cross-Temporal Sinhala OCR: Page-Level Adaptation and Diachronic Analysis

Sinhala is a morphologically rich abugida spoken by roughly 16 million people in Sri Lanka, and to date, there are no publicly available real-world datasets for page-level Sinhala OCR. All previous studies for assessing Sinhala OCR models have used artificially generated data.