Tagged articles

DeepSeek-OCR

6 articles · Page 1 of 1

Jun 2, 2026 · Artificial Intelligence

RTPrune: Two‑Stage Reading‑Inspired Token Pruning for Efficient DeepSeek‑OCR Inference

The paper presents RTPrune, a token‑pruning technique for DeepSeek‑OCR that exploits a two‑stage reading behavior in LLM decoding, first keeping high‑norm visual tokens and then fusing the rest via optimal‑transport matching with a dynamic pruning‑rate strategy, achieving up to 15% GFLOPs reduction and 18.9% speedup while preserving over 99% OCR accuracy across multiple benchmarks.

DeepSeek-OCROCR efficiencydynamic pruning

0 likes · 9 min read

RTPrune: Two‑Stage Reading‑Inspired Token Pruning for Efficient DeepSeek‑OCR Inference

Old Zhang's AI Learning

Jan 30, 2026 · Artificial Intelligence

PaddleOCR‑VL‑1.5: 0.9B Model Beats Billion‑Parameter OCR Models with 94.5% Accuracy

PaddleOCR‑VL‑1.5, the latest Baidu release, uses only 0.9 B parameters to achieve 94.5% accuracy on OmniDocBench v1.5, surpassing larger open‑source and commercial OCR models, while offering multi‑task, multi‑language support, lightweight deployment, and detailed performance benchmarks.

DeepSeek-OCRGPU inferenceOCR

0 likes · 9 min read

PaddleOCR‑VL‑1.5: 0.9B Model Beats Billion‑Parameter OCR Models with 94.5% Accuracy

HyperAI Super Neural

Nov 11, 2025 · Artificial Intelligence

How Deepseek-OCR Achieves SOTA Using Ultra‑Low Visual Token Counts

Deepseek-OCR leverages a visual‑compression approach, combining DeepEncoder and the DeepSeek3B‑MoE‑A570M decoder, to represent document text with far fewer visual tokens, achieving up to 97% OCR accuracy and surpassing GOT‑OCR2.0 and MinerU2.0 on OmniDocBench, while the article offers a one‑click deployment tutorial.

DeepEncoderDeepSeek-OCRLLM

0 likes · 6 min read

How Deepseek-OCR Achieves SOTA Using Ultra‑Low Visual Token Counts

AI2ML AI to Machine Learning

Nov 5, 2025 · Artificial Intelligence

Why No Perfect VLM OCR Exists for Complex Financial Reports – An In‑Depth Model Comparison

The article evaluates several VLM‑based OCR models on complex financial statements, comparing speed, layout accuracy, and handling of irregular tables, and concludes that while some models excel in specific aspects, none yet deliver a flawless solution for all scenarios.

DeepSeek-OCRInfinity-ParserMinerU-VLM

0 likes · 8 min read

Why No Perfect VLM OCR Exists for Complex Financial Reports – An In‑Depth Model Comparison

Fun with Large Models

Oct 26, 2025 · Artificial Intelligence

From Deep Learning to Large‑Model OCR: Which Model Leads the Pack?

This article traces OCR's evolution from early CNN‑LSTM systems to modern multimodal VLMs, analyzes leading open‑source models such as DeepSeek‑OCR, PaddleOCR, and MonkeyOCR, and offers practical guidance for long‑document, academic, and edge‑computing scenarios.

DeepSeek-OCRMonkeyOCROCR

0 likes · 15 min read

From Deep Learning to Large‑Model OCR: Which Model Leads the Pack?

AI2ML AI to Machine Learning

Oct 23, 2025 · Artificial Intelligence

Why Visually‑Rich Document Understanding Looks Like High‑End Docs: A Static Multimodal Overview

The article surveys the evolution of Visually‑Rich Document Understanding (VRDU), highlighting pioneering Chinese OCR research, the LayoutLM family, recent multimodal model breakthroughs, open‑source toolkits, and practical recommendations for handling diverse document types and tasks.

DeepSeek-OCRLayoutLMMultimodal OCR

0 likes · 11 min read

Why Visually‑Rich Document Understanding Looks Like High‑End Docs: A Static Multimodal Overview