A Survey on Vietnamese Document Analysis and Recognition: Challenges and Future Directions

TL;DR

This survey reviews Vietnamese document analysis and recognition, highlighting challenges like diacritics and data scarcity, and explores how recent large language and vision-language models can advance the field.

Contribution

It provides a comprehensive overview of current techniques, identifies key limitations, and discusses future directions including dataset creation and multimodal approaches.

Findings

Deep learning shows promise but is limited by data scarcity.

LLMs and vision-language models improve Vietnamese text recognition.

Future research should focus on dataset development and model optimization.

Abstract

Vietnamese document analysis and recognition (DAR) is a crucial field with applications in digitization, information retrieval, and automation. Despite advancements in OCR and NLP, Vietnamese text recognition faces unique challenges due to its complex diacritics, tonal variations, and lack of large-scale annotated datasets. Traditional OCR methods often struggle with real-world document variations, while deep learning approaches have shown promise but remain limited by data scarcity and generalization issues. Recently, large language models (LLMs) and vision-language models have demonstrated remarkable improvements in text recognition and document understanding, offering a new direction for Vietnamese DAR. However, challenges such as domain adaptation, multimodal learning, and computational efficiency persist. This survey provide a comprehensive review of existing techniques in Vietnamese document recognition, highlights key limitations, and explores how LLMs can revolutionize the field. We discuss future research directions, including dataset development, model optimization, and the integration of multimodal approaches for improved document intelligence. By addressing these gaps, we aim to foster advancements in Vietnamese DAR and encourage community-driven solutions.