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

arXiv: https://arxiv.org/abs/2506.05061 · HTML: https://mlsanity.com/paper/2506.05061/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 46 references that are in the map.
- First posted on arXiv 5 Jun 2025.

## Builds on (most cited first, 13 of 13)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [OCR-free Document Understanding Transformer](https://mlsanity.com/paper/2111.15664/) (2021)
- [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://mlsanity.com/paper/2109.10282/) (2021)
- [Character Region Awareness for Text Detection](https://mlsanity.com/paper/1904.01941/) (2019)
- [PP-OCR: A Practical Ultra Lightweight OCR System](https://mlsanity.com/paper/2009.09941/) (2020)
- [PhoBERT: Pre-trained language models for Vietnamese](https://mlsanity.com/paper/2003.00744/) (2020)
- [Efficient and Accurate Arbitrary-Shaped Text Detection with Pixel Aggregation Network](https://mlsanity.com/paper/1908.05900/) (2019)
- [PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networks](https://mlsanity.com/paper/2004.07464/) (2020)
- [Vintern-1B: An Efficient Multimodal Large Language Model for Vietnamese](https://mlsanity.com/paper/2408.12480/) (2024)
- [ViOCRVQA: Novel Benchmark Dataset and Vision Reader for Visual Question Answering by Understanding Vietnamese Text in Images](https://mlsanity.com/paper/2404.18397/) (2024)
- [UIT-HWDB: Using Transferring Method to Construct A Novel Benchmark for Evaluating Unconstrained Handwriting Image Recognition in Vietnamese](https://mlsanity.com/paper/2211.05407/) (2022)

## Abstract (arXiv)

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.