Logics-Parsing: Advanced Document Parsing for Complex Layouts

In both work and study, extracting usable content from images or PDFs is often frustrating — especially when tools struggle with:

• Converting messy handwritten content into clean notes
• Importing tables from references into presentation slides
• Editing papers with specialized formats (e.g., chemistry)

Even the latest Large Vision-Language Models (LVLMs) show limitations in understanding multi-column layouts, mixed content, and scientific formulas, often failing to preserve proper reading order.

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Alibaba’s Breakthrough: Logics-Parsing

At the September Yunqi Conference, Alibaba’s Data Technology and Product Department (iOrange Technology) officially released and open-sourced Logics-Parsing — a robust PDF parsing tool.

Key innovations include:

• Use of a high-quality, challenging dataset
• Introduction of Layout-Centric Reinforcement Learning (LC-RL)
• A “SFT-then-RL” two-stage training strategy for logical reading path planning

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Resources

• GitHub: https://github.com/alibaba/Logics-Parsing
• Online Demo: https://www.modelscope.cn/studios/Alibaba-DT/Logics-Parsing/summary
• Technical Report: https://arxiv.org/abs/2509.19760

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What is Logics-Parsing?

Logics-Parsing is built on the Qwen2.5-VL architecture and is trained on a diverse data mix — including chemical formulas and handwritten Chinese — boosting document parsing generalization.

Capabilities

• Complex layout analysis with accurate reading order inference
• Extraction of text, tables, formulas, handwriting, and chemical structures
• Outputs in `qwen-html` or `mathpix-markdown` format

Result: Solves the “last mile” in document analysis, achieving SOTA results across varied real-world scenarios.

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How Layout-Centric Reinforcement Learning Works

LC-RL uses Group Relative Policy Optimization (GRPO) — ideal for structured output optimization.

Training Process:

• Parse predicted and ground-truth outputs to identify text and bounding boxes
• Compute three distinct rewards:
• Text Accuracy: Character-level similarity via negative normalized Levenshtein distance
• Localization Accuracy: Bounding box alignment quality
• Reading Logic: Penalizes misordered content using inversion counts

These rewards are linearly combined into a comprehensive signal for policy optimization.

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Understanding the “SFT-then-RL” Two-Stage Strategy

This approach mirrors student learning:

• Stage 1 – SFT: Train with a gold-standard dataset to master fundamentals
• Stage 2 – RL: Tackle high-complexity cases with structured, stepwise guidance and multi-dimensional performance metrics — rewarding correct step execution

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Core Highlights

(1) Effortless End-to-End Processing

• Single-step pipeline from document images to structured output
• Optimized for challenging layouts

(2) Advanced Content Recognition

• Scientific formulas & handwritten text
• Chemical structures with SMILES format output

(3) Rich Structured Output

• Qwen HTML preserving structure and order
• Tagged content blocks with type, coordinates, and OCR text
• Removes non-core elements (e.g., headers/footers)

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Practical Examples

Mathematical Formula Reproduction

• Maintains semantic integrity & layout fidelity

Chemical Structure Restoration

• Parses atomic topology & bond types, supports SMILES export

Complex Table Parsing

• Preserves merged cells & exact structure

Handwriting Recognition

• Detects cursive, mixed styles, preserves structure

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Outstanding Results

Logics-Parsing achieves SOTA performance in:

• Text parsing accuracy
• Chemical structure recognition
• Handwritten content processing

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Extending to Multi-Platform Publishing

Platforms like AiToEarn官网 integrate:

• AI document parsing tools
• Content generation and multi-platform publishing
• Monetization & analytics
• Supports Douyin, Kwai, WeChat, Bilibili, Rednote, Facebook, Instagram, LinkedIn, Threads, YouTube, Pinterest, X (Twitter).

More resources:

• AiToEarn博客
• AiToEarn开源地址
• AI模型排名

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Logics-Parsing Project Overview

ModelScope: https://www.modelscope.cn/studios/Alibaba-DT/Logics-Parsing/summary

GitHub: https://github.com/alibaba/Logics-Parsing

Introduction

Developed by Alibaba DT Team, Logics-Parsing provides logic parsing for:

• Natural language understanding
• Question answering
• Semantic reasoning

Key Features

• Logic Understanding: Converts language to structured logic
• Easy Integration: Embed into apps for QA and automation
• Extensible: Custom parsing rules for domains
• ModelScope Access: Test and deploy online

Use Cases

• QA Systems: Improve query understanding
• Semantic Search: Enhance retrieval accuracy
• Business Rule Automation: Convert instructions to rules

Related Tools

AiToEarn complements parsing projects with:

• AI content generation
• Cross-platform publishing
• Model ranking & analytics

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If you like, I can create a clean visual comparison table showing Logics-Parsing vs traditional OCR models for better readability. Would you like me to add that?