SOTA OCR with Core ML and dots.ocr

Finetuning olmOCR to be a faithful OCR-Engine

Introducing Mistral OCR 3

Supercharge your OCR Pipelines with Open Models

Mistral OCR

Color Independent Word Segmentation From Transcribed Bangla Passages

The paper presents a color‑independent word segmentation method for handwritten Bangla text images. It works on smartphone‑captured images regardless of paper color or ink type, and the custom dataset includes various real‑world challenges such as shadows. The system achieves 90.60 % recall, 91.80 % precision, and 91.20 % F1‑score on 7,374 words.

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).

Introducing Mistral OCR 4

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

TongGuOCR: A Layout-Aware and Token-Augmented OCR Framework for Chinese Historical Documents

arXiv:2608. 07917v1 Announce Type: new Abstract: Chinese historical documents preserve valuable cultural heritage, but many collections remain accessible only as scanned page images, preventing full-text retrieval, collation, and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

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

Systematic Literature Review of Machine Learning Models and Applications for Text Recognition

This systematic literature review examines 97 studies on optical character recognition (OCR) from 2015 to 2025, tracing the evolution of AI models, application domains, data types, and linguistic coverage. It identifies key OCR models, evaluates their performance, strengths, and limitations, and highlights unresolved challenges such as limited resources for underrepresented languages, high variability in handwritten text, and constraints in real‑time applications. The review proposes promising approaches—including self‑supervised learning, multimodal AI, AutoML, AI‑assisted postprocessing, TinyML, and joint corpora creation—to enhance OCR accuracy and address these challenges for industrial use.

TongGuOCR: A Layout-Aware and Token-Augmented OCR MLLM for Chinese Historical Documents

arXiv:2608. 07917v2 Announce Type: replace Abstract: Chinese historical documents preserve valuable cultural heritage, but many collections remain accessible only as scanned page images, preventing full-text retrieval, collation, and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.