GLM-OCR: The Ultimate Guide to the 0.9B Lightweight OCR Powerhouse for Complex Documents
5 days ago 高效码农GLM-OCR: A 0.9B Lightweight Multimodal OCR Model — Complete Guide to Performance, Deployment & Practical Use Abstract: GLM-OCR is a multimodal OCR model with only 0.9B parameters. It achieved a top score of 94.62 on OmniDocBench V1.5, supports deployment via vLLM, SGLang, and Ollama, delivers a PDF parsing throughput of 1.86 pages/second, adapts to complex document scenarios, and balances efficient inference with high-accuracy recognition. Introduction: Why GLM-OCR Stands Out as the Top Choice for Complex Document OCR? If you’re a developer working on document processing or data extraction, you’ve likely faced these pain points: Traditional OCR models struggle with low …
DeepSeek-OCR 2: The AI That Reads Documents Like a Human Using Visual Causal Flow
11 days ago 高效码农DeepSeek-OCR 2: Visual Causal Flow – A New Chapter in Human-Like Visual Understanding Core Question: How can traditional Vision-Language Models (VLMs) break free from rigid raster-scan limitations to achieve document understanding based on “Visual Causal Flow”? In the rapidly evolving landscape of multimodal large models, we have grown accustomed to treating images as static 2D matrices, converting them into 1D token sequences for input into Large Language Models (LLMs). However, does the default “top-left to bottom-right” rigid processing really align with human intuition when reading complex documents? When facing academic PDFs containing formulas, tables, multi-column layouts, or complex logical structures, …
Training Document AI: The LightOnOCR-mix-0126 Dataset Explained
18 days ago 高效码农The LightOnOCR-mix-0126 Dataset: The Foundation for Next-Generation Document AI Have you ever wondered how AI models that can “read” complex academic papers, accurately extract table data, and even understand intricate mathematical formulas are trained? The secret lies in a high-quality, large-scale, and precisely annotated training dataset. Today, we delve into a dataset quietly playing a pivotal role in the field of document intelligence: 「LightOnOCR-mix-0126」. It’s not merely a collection of text and images; it represents a cutting-edge methodology for generating high-quality OCR training data through “distillation.” What is LightOnOCR-mix-0126? In simple terms, LightOnOCR-mix-0126 is a large-scale dataset specifically constructed for …
MegaRAG: Build Multimodal RAG That Understands Charts & Slides Like a Human
1 months ago 高效码农MegaRAG: Teaching RAG to Read Diagrams, Charts, and Slide Layouts Like a Human “ What makes MegaRAG different? It treats every page as a mini-multimodal graph—text, figures, tables, and even the page screenshot itself become nodes. A two-pass large-language-model pipeline first extracts entities in parallel, then refines cross-modal edges using a global subgraph. The final answer is produced in two stages to prevent modality bias. On four public benchmarks the system outperforms GraphRAG and LightRAG by up to 45 percentage points while running on a single RTX-3090. § The Core Question This Article Answers “How can I build a retrieval-augmented-generation …
Qwen3-VL: How a 256K-Token Vision Model Masters 500-Page Documents
2 months ago 高效码农Inside Qwen3-VL: How a 256K-Token Vision-Language Model Learns to Read 500-Page Documents and 2-Hour Videos Without Breaking a Sweat A plain-language walk-through of the technical report that introduced Qwen3-VL—no hype, no jargon, and no external facts beyond the original paper. Table of Contents The 30-Second Takeaway Model Family at a Glance Three Architectural Tweaks That Actually Matter Four-Stage Training From Scratch What the Model Was Fed (Data Ingredients) Post-Training: SFT, Distillation, and Reinforcement Learning “Thinking Mode” Explained Benchmark Scores in One Sitting Hardware-Friendly Deployment Answers to the Most-Asked Questions Key Limits and Next Steps 1. The 30-Second Takeaway Qwen3-VL is …
DeepSeek-OCR 3B Vision Language Model Deployment Guide | Fine-tuning Vision Transformer for Document AI
3 months ago 高效码农DeepSeek-OCR: How to Run & Fine-tune for Real-World Document Intelligence How can you effectively deploy and customize DeepSeek-OCR, a 3B-parameter vision model, to achieve production-grade document understanding with minimal resource overhead? The answer lies in understanding its unique architecture—contextual optical compression that converts 2D layouts into efficient vision tokens—and leveraging two distinct but complementary deployment paths: vLLM for service-oriented stability and Unsloth for performance-optimized inference. This guide walks through both approaches, then demonstrates how just 60 training steps on a domain-specific dataset can slash error rates by 88%, turning a capable generalist into a highly accurate specialist. What Makes DeepSeek-OCR …
IBM Granite-Docling-258M: The Open-Source Document AI Model Revolutionizing Enterprise Document Processing
4 months ago 高效码农Granite Docling Logo Introduction: The Challenge of Document Understanding in the Digital Age In today’s enterprise environments, organizations process countless documents daily—contracts, reports, academic papers, technical manuals, and more. While traditional optical character recognition (OCR) technologies can extract text from these documents, they often fail to preserve the underlying structure: tables become disorganized, mathematical formulas render incorrectly, code snippets lose their formatting, and even paragraph sequencing can become disrupted. This structural loss significantly reduces information retrieval efficiency and creates substantial challenges for automated document processing pipelines. IBM’s recently released Granite-Docling-258M represents a transformative approach to these challenges. This completely open-source, …