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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- MegaHan97K Public
MegaHan97K: A Large-Scale Dataset for Mega-Category Chinese Character Recognition with over 97K Categories
SCUT-DLVCLab/MegaHan97K’s past year of commit activity - Document-AI-Recommendations Public
Algorithms, papers, datasets, performance comparisons for Document AI. Continuously updating.
SCUT-DLVCLab/Document-AI-Recommendations’s past year of commit activity - SCUT-EnsExam Public
SCUT-EnsExam is a real-world handwritten text erasure dataset for examination paper scenarios, which consists of 545 examination paper images. The dataset is randomly divided into training set and test set of 430 and 115 images, respectively.
SCUT-DLVCLab/SCUT-EnsExam’s past year of commit activity - GPT-4V_OCR Public
Evaluation of the Optical Character Recognition (OCR) capabilities of GPT-4V(ision)
SCUT-DLVCLab/GPT-4V_OCR’s past year of commit activity
