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May 9, 2026 · Artificial Intelligence

Why PDF Parsing Is Hard for RAG and Which Mainstream Solutions Work

The article examines the intrinsic challenges of extracting structured text from PDFs for Retrieval‑Augmented Generation—such as missing reading order, table reconstruction, font encoding, and scanned images—and compares lightweight libraries, AI‑enhanced frameworks, commercial APIs, and visual language models as practical solutions.

AI frameworksOCRPDF parsing

0 likes · 23 min read

Why PDF Parsing Is Hard for RAG and Which Mainstream Solutions Work

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.

GPU inferenceMulti-languageOCR

0 likes · 9 min read

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

Data Party THU

Nov 5, 2025 · Artificial Intelligence

How VLM‑FO1 Turns Vision‑Language Models into Precise Perception Machines

VLM‑FO1 introduces a generate‑plus‑reference paradigm that replaces coordinate generation with region token referencing, adding plug‑in modules such as a proposal generator, a hybrid fine‑grained encoder, and a region‑language connector to give any pretrained visual language model accurate, fine‑grained perception while preserving its original capabilities.

AI researchMultimodalPlug-and-Play

0 likes · 15 min read

How VLM‑FO1 Turns Vision‑Language Models into Precise Perception Machines

AI Algorithm Path

Jul 20, 2025 · Artificial Intelligence

How to Build an Open‑Set Object Detection Workflow: A Comprehensive Guide

This article presents a step‑by‑step agentic object detection pipeline that combines open‑vocabulary detectors such as Grounding‑DINO with visual language models (GPT‑4o, o1) for concept extraction, critique, refinement, and validation, complete with code snippets, design rationale, and real‑world examples.

Grounding DINOPipelinePython

0 likes · 33 min read

How to Build an Open‑Set Object Detection Workflow: A Comprehensive Guide

Sohu Tech Products

Jan 8, 2025 · Artificial Intelligence

Multimodal RAG: Implementation Paths and Development Prospects

The talk outlines Multimodal RAG implementation routes, comparing OCR‑based object recognition, transformer encoder‑decoder encoding, and Visual Language Model processing, explains the ColPali late‑interaction method for multi‑dimensional vector matching, addresses scaling tensors with binarization and reranking, and recommends a hybrid long‑term strategy where VLM excels on abstract imagery while traditional OCR remains valuable.

ColPaliDocument ProcessingMultimodal RAG

0 likes · 10 min read

Multimodal RAG: Implementation Paths and Development Prospects

Baobao Algorithm Notes

Nov 18, 2024 · Artificial Intelligence

Boosting Vision‑Language Model Performance: Prompt‑First vs. Fine‑Tuning Strategies

This guide explains when to rely on prompt engineering versus SFT fine‑tuning for Vision‑Language Models, emphasizing data quality, appropriate dataset sizes, training epochs, hyper‑parameter tuning, and practical steps to build robust VLM pipelines.

AIDPOData Quality

0 likes · 10 min read

Boosting Vision‑Language Model Performance: Prompt‑First vs. Fine‑Tuning Strategies

AI Large Model Application Practice

Oct 14, 2024 · Artificial Intelligence

Build a Multimodal RAG Pipeline with Kotaemon, Azure Document Intelligence, and VLM

This guide walks through setting up the open‑source Kotaemon framework, configuring Azure Document Intelligence and a visual large model, and implementing code to extract and caption images and tables from PDFs for end‑to‑end multimodal RAG applications.

AzurePythonRAG

0 likes · 12 min read

Build a Multimodal RAG Pipeline with Kotaemon, Azure Document Intelligence, and VLM