Towards a Joint Khmer Text Recognition and Word Segmentation

2026-08-30

AI Summary

本文提出了一种联合高棉语文本识别与分词的统一模型，使用CTC解码器解决了传统流程中需要两个独立模型导致的易出错和延迟问题。

Abstract

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.

Problem

• Khmer
• Text Recognition
• Word Segmentation

Innovation

• Joint Khmer Text Recognition and Word Segmentation
• Unified Model
• Connectionist-Temporal-Classification (CTC)
• Fast Parallel Decoding