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VNProductKIE: A Dataset and Three-Stage Pipeline for Key Product Information Recognition on Vietnamese Packaging Labels
Author: Lê Thành Tiến, Lưu Hữu Trí, Hà Trọng Nguyễn
Abstract: Food waste poses serious environmental and economic concerns, often worsened by the lack of accessible product information. Automated extraction from packaging labels offers a promising solution, yet existing datasets fall short in representing the linguistic and visual diversity found in Vietnamese markets. This paper introduces VNProductKIE, a new dataset of high-resolution images capturing Vietnamese food and beverage packaging. It features both English and Vietnamese text, diacritic-rich scripts, local date formats, and real-world distortions such as blur, curvature, and clutter. To extract structured information, this paper proposes a three-stage pipeline including: (1) a YOLOv11-based detector for locating key regions (e.g., product name, weight, brand, expiration date), (2) a word-level detector for segmenting individual words, and (3) a VietOCR-based recognizer for transcription. The final output is structured into complete product metadata. This pipeline experimented on VNProductKIE achieved a recognition accuracy of 98.85%, highlighting the effectiveness of the proposed approach.
The 14th International Symposium on Information and Communication Technology: the 14th International Conference on Information and Communication Technology, focusing on areas such as AI and Big Data Platforms, Networking and Communications, Multimedia Processing, AI Applications, Generative AI, Operations Research and Optimization, as well as Cybersecurity. The conference will be held in Nha Trang, Vietnam, from December 12–14, 2025, providing an academic forum for researchers and scholars to share the latest findings and discuss future challenges in computer science.
An Overview of the Effectiveness of Graph Learning Methods for Traffic Demand Forecasting
Author: Lương Chí Trung, Chung Thái Kiệt
Abstract: Traffic demand forecasting plays a crucial role in intelligent transportation systems and is a fundamental aspect of smart cities. The spatio-temporal nature of this task poses significant challenges for forecasting models, especially when it comes to extracting spatial features from complex graphs. To effectively capture these intricate spatial patterns, previous studies have explored a variety of methods for constructing a graph from spatial data. In this study, we present a thorough survey and taxonomy of existing research based on various graph construction methods, including static, adaptive, and dynamic approaches. To thoroughly evaluate these models and methodologies, we conduct experiments on seven real-world datasets. Among these, two are widely recognized benchmarks, while the other five have been newly collected and processed by us from government open data platforms. Our findings enable us to analyze and compare the strengths and limitations of various approaches. We also identify emerging trends and assess the current effectiveness of these methods. Finally, we propose potential research directions and opportunities for future work in this field.