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DocXChain: A Powerful Open-Source Toolchain for Document Parsing and Beyond

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arxiv 2310.12430 v1 pith:PLRRRHH7 submitted 2023-10-19 cs.CV cs.CL

classification cs.CVcs.CL
keywords docxchaindocumentparsingtextpowerfulapplicationsbasiccapabilities
verification ladder T0 review T1 audit T2 compute T3 formal
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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:~\url{https://github.com/AlibabaResearch/AdvancedLiterateMachinery/tree/main/Applications/DocXChain}

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Improving MLLM's Document Image Machine Translation via Synchronously Self-reviewing Its OCR Proficiency

    cs.CL 2025-07 conditional novelty 6.0 of 10

    A fine-tuning paradigm that prompts MLLMs to self-generate OCR text before translating document images improves DIMT quality and reduces catastrophic forgetting of OCR.

  2. Single-to-mix Modality Alignment with Multimodal Large Language Model for Document Image Machine Translation

    cs.CL 2025-07 conditional novelty 6.0 of 10

    M4Doc distills the multimodal representations of a frozen MLLM into an image-only encoder, improving document image translation quality and generalization without requiring the MLLM at inference.

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