Chan-Su Lee

About

Chan-Su Lee is a leading researcher in computer vision and human–computer interaction, with a primary focus on facial expression recognition and multilingual text detection in natural scenes. His most influential work introduces a hybrid framework that combines convolutional neural networks (CNNs) with support vector machines (SVM) for facial expression recognition, achieving robust performance for emotion-aware systems, robot interfaces, and smart agents. This paper has garnered 38 citations, underscoring its impact on affective computing and interactive AI. Lee’s contributions extend to bilingual text recognition, where he developed a CNN–recurrent neural network (RNN) architecture paired with a connectionist temporal classification (CTC) decoder to detect and recognize Urdu and English text in complex natural scene images. This work addresses critical challenges in autonomous navigation, language translation, and assistive technologies. By bridging deep learning with practical applications in multilingual environments, Lee’s research advances both theoretical understanding and real-world deployment of vision-based systems. His work is essential reading for students and researchers interested in emotion recognition, scene text understanding, and cross-modal AI systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Hybrid Approach for Facial Expression Recognition Using Convolutional Neural Networks and SVM

   38 citations · 2022
2. 2
   Detection and Recognition of Bilingual Urdu and English Text in Natural Scene Images Using a Convolutional Neural Network–Recurrent Neural Network Combination with a Connectionist Temporal Classification Decoder

   2 citations · 2025

Key Collaborators

Contact & Links