DocXChain: A Powerful Open-Source Toolchain for Document Parsing and Beyond
원문 초록
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}
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
DoPTA: Improving Document Layout Analysis using Patch-Text Alignment
Ocean-OCR: Towards General OCR Application via a Vision-Language Model
TextSSR: Diffusion-based Data Synthesis for Scene Text Recognition
SVTRv2: CTC Beats Encoder-Decoder Models in Scene Text Recognition
Stratified Domain Adaptation: A Progressive Self-Training Approach for Scene Text Recognition
PdfTable: A Unified Toolkit for Deep Learning-Based Table Extraction
토론
이 논문과 연결된 포럼 글이 아직 없습니다.