统一模型同时识别高棉文字符与词边界，省去传统分步处理。
Towards a Joint Khmer Text Recognition and Word Segmentation
- 用CTC解码器设计联合识别与分词模型，支持并行快速推理。
- 在文档、场景和手写图像上均实现字符识别与词边界定位。
- 适用于需要高效处理高棉文的自动化文档系统。
文本识别，即从文档图像中提取电子文本，对知识检索任务（如检索增强生成）至关重要。对于高棉文，由于没有可见的词分隔符，提取文本后需额外进行词分割步骤。传统的识别-分割流水线需两个独立序列模型，不仅容易出错，且在大规模文档处理中引入显著延迟。本文提出一种新颖的统一模型，实现高棉文文本识别与词分割的联合建模。所提模型采用连接时序分类（CTC）解码器，支持快速并行解码，可被指令以（b=1）或不（b=0）包含词边界的方式识别高棉文。在不同模态的基准数据集（文档、场景和手写图像）上的实验表明，该模型不仅能准确识别字符，还能精确定位词边界，从而消除传统流水线中额外的词分割步骤。
原文摘要 · Abstract (English)
Text recognition, or extracting electronic text from document images, has been indispensable for knowledge retrieval tasks, such as retrieval-augmented generation (RAG). For Khmer, extracted text is subject to an extra word segmentation step, as Khmer does not use any visible word delimiters to denote word boundaries. Thus, a recognition-then-segmentation pipeline for Khmer requires two separate sequential models; this is not only error-prone but also adds significant latency for large-scale document processing. This paper proposes a novel joint Khmer text recognition and word segmentation framework in a unified model. The proposed model, using a connectionist-temporal-classification (CTC) decoder for fast, parallel decoding, can be instructed to recognize Khmer text with ($b=1$) and without ($b=0$) word segmentation. Experimental results on different benchmark datasets of different document modalities (document, scene, and handwritten images) show that the proposed model can not only recognize characters in document images but also locate word boundaries, removing the need for an extra word segmentation step in a conventional sequential pipeline.
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