DocXChain: A Powerful Open-Source Toolchain for Document Parsing and Beyond
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Abstract (arXiv)
In this report, we introduce DocXChain, a powerful open-source toolchain for document parsing, which is designed and developed to automatically convert the rich information embodied in unstructured documents, such as text, tables and charts, into structured representations that are readable and manipulable by machines. Specifically, basic capabilities, including text detection, text recognition, table structure recognition and layout analysis, are provided. Upon these basic capabilities, we also build a set of fully functional pipelines for document parsing, i.e., general text reading, table parsing, and document structurization, to drive various applications related to documents in real-world scenarios. Moreover, DocXChain is concise, modularized and flexible, such that it can be readily integrated with existing tools, libraries or models (such as LangChain and ChatGPT), to construct more powerful systems that can accomplish more complicated and challenging tasks. The code of DocXChain is publicly available at:~urlhttps://github.com/AlibabaResearch/AdvancedLiterateMachinery/tree/main/Applications/DocXChain
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 5 of its 11 references that are in the map. First posted on arXiv 19 Oct 2023.
Builds on
5 of 11 references.
References · 5
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2023The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)cited by 677
- 2018Scene Text Detection and Recognition: The Deep Learning Eracited by 114
- 2021Document AI: Benchmarks, Models and Applicationscited by 61
- 2023PDFTriage: Question Answering over Long, Structured Documentscited by 29
Built on by
Citing papers per year: 2024: 3; 2025: 2; 2026: 3.
8 papers.
- 2024MinerU: An Open-Source Solution for Precise Document Content Extractioncited by 255
- 2024OmniDocBench: Benchmarking Diverse PDF Document Parsing with Comprehensive Annotationscited by 144
- 2024DocGenome: An Open Large-scale Scientific Document Benchmark for Training and Testing Multi-modal Large Language Modelscited by 34
- 2026DocDancer: Towards Agentic Document-Grounded Information Seekingcited by 11
- 2025Improving MLLM's Document Image Machine Translation via Synchronously Self-reviewing Its OCR Proficiencycited by 5
- 2025Single-to-mix Modality Alignment with Multimodal Large Language Model for Document Image Machine Translationcited by 4
- 2026MARDoc: A Memory-Aware Refinement Agent Framework for Multimodal Long Document QAcited by 1
- 2026"Are You Sure?": An Empirical Study of Human Perception Vulnerability in LLM-Driven Agentic Systemscited by 0
- 2026MARDoc: A Memory-Aware Refinement Agent Framework for Multimodal Long Document QA4 Jun 2026
- 2026"Are You Sure?": An Empirical Study of Human Perception Vulnerability in LLM-Driven Agentic Systems24 Feb 2026
- 2026DocDancer: Towards Agentic Document-Grounded Information Seeking8 Jan 2026
- 2025Improving MLLM's Document Image Machine Translation via Synchronously Self-reviewing Its OCR Proficiency11 Jul 2025
- 2025Single-to-mix Modality Alignment with Multimodal Large Language Model for Document Image Machine Translation10 Jul 2025
- 2024OmniDocBench: Benchmarking Diverse PDF Document Parsing with Comprehensive Annotations10 Dec 2024
- 2024MinerU: An Open-Source Solution for Precise Document Content Extraction27 Sep 2024
- 2024DocGenome: An Open Large-scale Scientific Document Benchmark for Training and Testing Multi-modal Large Language Models17 Jun 2024