The Deep Learning and Vision Computing Lab is dedicated to advanced theoretical research and innovative applications in the fields of artificial intelligence, computer vision, machine learning, and pattern recognition. Our current research focuses on deep learning, text detection and recognition, document analysis and understanding, and artificial intelligence. In recent years, our team has led more than 30 national and provincial research projects, making significant achievements in optical character recognition (OCR), handwriting recognition, gesture recognition and interaction technology, and innovative applications of deep learning. We have published over 300 SCI/EI papers, obtained more than 50 authorized invention patents, won 5 provincial and ministerial science and technology awards, and achieved first place in international academic competitions 4 times.
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- OCR-Reasoning Public
[arXiv: 2505.17163] OCR-Reasoning Benchmark: Unveiling the True Capabilities of MLLMs in Complex Text-Rich Image Reasoning
SCUT-DLVCLab/OCR-Reasoning’s past year of commit activity - PAVENet Public
[IEEE TPAMI 2025] Privacy-Preserving Biometric Verification With Handwritten Random Digit String
SCUT-DLVCLab/PAVENet’s past year of commit activity - MegaHan97K Public
[PR 2025] The official GitHub page of "MegaHan97K: A Large-Scale Dataset for Mega-Category Chinese Character Recognition with over 97K Categories"
SCUT-DLVCLab/MegaHan97K’s past year of commit activity - AutoScaler Public
[PR 2026] The official GitHub page of "AutoScaler: Self Scale Alignment for Handwritten Mathematical Expression Recognition"
SCUT-DLVCLab/AutoScaler’s past year of commit activity - ACP-RAG Public
[NAACL 2025] Large-Scale Corpus Construction and Retrieval-Augmented Generation for Ancient Chinese Poetry: New Method and Data Insights (ACP-Corpus; ACP-QA; ACP-RAG)
SCUT-DLVCLab/ACP-RAG’s past year of commit activity
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