BabelDOC: 通过中间表示实现更好的布局保持PDF翻译
BabelDOC: Better Layout-Preserving PDF Translation via Intermediate Representation
AI总结：
BabelDOC通过中间表示框架实现布局保持的PDF翻译，解决语言处理与布局保持的矛盾，提升翻译精度和文档一致性。
摘要：
随着全球跨语言交流的加剧，视觉丰富文档如PDF中的语言障碍仍是一个实际瓶颈。现有文档翻译流程面临语言处理与布局保持之间的张力：以文本为导向的计算机辅助翻译（CAT）系统通常丢弃结构元数据，而文档解析器专注于提取而不支持翻译后的忠实重排。我们介绍了BabelDOC，一种基于中间表示（IR）的框架，用于布局保持的PDF翻译。BabelDOC将视觉布局元数据与语义内容解耦，使文档层面的翻译操作如术语提取、跨页上下文处理、术语表约束生成和公式占位符生成成为可能。翻译后的内容通过自适应排版引擎重新锚定到原始布局。在经过精心挑选的200页基准测试集上，结合人工评估和多模态LLM-as-judge评估，实验表明BabelDOC在布局保真度、视觉美观性和术语一致性方面优于代表性基线，同时保持了竞争性的翻译精度。开源工具包及其交互式下游应用已公开发布，并吸引了超过8.4K GitHub星标和17名贡献者。演示视频也已提供。
英文摘要：
As global cross-lingual communication intensifies, language barriers in visually rich documents such as PDFs remain a practical bottleneck. Existing document translation pipelines face a tension between linguistic processing and layout preservation: text-oriented Computer-Assisted Translation (CAT) systems often discard structural metadata, while document parsers focus on extraction and do not support faithful re-rendering after translation. We introduce BabelDOC, an Intermediate Representation (IR)-based framework for layout-preserving PDF translation. BabelDOC decouples visual layout metadata from semantic content, enabling document-level translation operations such as terminology extraction, cross-page context handling, glossary-constrained generation, and formula placeholdering. The translated content is then re-anchored to the original layout through an adaptive typesetting engine. Experiments on a curated 200-page benchmark, together with human evaluation and multimodal LLM-as-a-judge evaluation, show that BabelDOC improves layout fidelity, visual aesthetics, and terminology consistency over representative baselines, while maintaining competitive translation precision. The open-source toolkit and its interactive downstream applications are publicly available and have attracted over 8.4K GitHub stars and 17 contributors at the time of writing. A demonstration video is also available.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
