Towards Universal Khmer Text Recognition

TL;DR

This paper introduces a universal Khmer text recognition framework that effectively handles multiple text modalities using a novel adaptive feature selection technique, achieving state-of-the-art results and providing a new benchmark for future research.

Contribution

We propose a novel modality-aware adaptive feature selection method enabling a single model to recognize diverse Khmer text modalities, improving robustness and efficiency.

Findings

Achieved state-of-the-art performance across multiple Khmer text modalities.

Developed the first comprehensive benchmark for universal Khmer text recognition.

Demonstrated the effectiveness of MAFS in enhancing recognition robustness.

Abstract

Khmer is a low-resource language characterized by a complex script, presenting significant challenges for optical character recognition (OCR). While document printed text recognition has advanced because of available datasets, performance on other modalities, such as handwritten and scene text, remains limited by data scarcity. Training modality-specific models for each modality does not allow cross-modality transfer learning, from which modalities with limited data could otherwise benefit. Moreover, deploying many modality-specific models results in significant memory overhead and requires error-prone routing each input image to the appropriate model. On the other hand, simply training on a combined dataset with a non-uniform data distribution across different modalities often leads to degraded performance on underrepresented modalities. To address these, we propose a universal Khmer text recognition (UKTR) framework capable of handling diverse text modalities. Central to our method is a novel modality-aware adaptive feature selection (MAFS) technique designed to adapt visual features according to a particular input image modality and enhance recognition robustness across modalities. Extensive experiments demonstrate that our model achieves state-of-the-art (SoTA) performance. Furthermore, we introduce the first comprehensive benchmark for universal Khmer text recognition, which we release to the community to facilitate future research. Our datasets and models can be accessible via this gated repository\footnote{in review}.