Description
This paper presents a lightweight AI-based approach for converting handwritten mathematical expressions, offering an efficient alternative to traditional scanning methods. Our model addresses the inherent challenges of math handwriting recognition, including the two-dimensional structure and symbol ambiguity, by leveraging deep learning techniques optimized for spatial representation. Trained on a diverse dataset, including samples from MathWriting, the system achieves an average recognition accuracy of 90%, significantly outperforming the 64% industry benchmark. Evaluation across four key mathematical terms—Function, Generality, Hyperbolic, and Derivative—demonstrates reliable character-level recognition and consistent differentiation of visually similar symbols. With processing times ranging from 1 to 2 seconds depending on input complexity, the model supports real-time application and user interaction. Implementation insights suggest the use of convolutional neural networks (CNNs) or similar architectures, with a processing pipeline optimized for both accuracy and speed. Future directions include expanding the dataset, enhancing symbol-specific accuracy, and integrating adaptive learning for personalized recognition. This work establishes a practical foundation for deploying AI-powered math handwriting recognition in educational and research environments, emphasizing usability, responsiveness, and improved performance over conventional methods.
Thông tin các tác giả
1/ Cao Trọng Nghĩa: Đang theo học trường Phổ thông Năng khiếu ĐH Quốc gia TP.HCM, 153 Đ. Nguyễn Chí Thanh, Quận 5, Hồ Chí Minh, email: student240723@ptnk.edu.vn
2/ Nguyễn Phước Hiền: Đang theo học trường Phổ thông Năng khiếu ĐH Quốc gia TP.HCM, 153 Đ. Nguyễn Chí Thanh, Quận 5, Hồ Chí Minh, email: student240205@ptnk.edu.vn
3/ Nguyễn Hoàng Ánh Linh: Đang công tác tại trường ĐH Khoa học Tự nhiên ĐH Quốc gia TP.HCM, 227 Đ. Nguyễn Văn Cừ, Phường 4, Quận 5, Hồ Chí Minh, email:nhalinh@hcmus.edu.vn
Từ khóa
Math Handwriting Recognition, Artificial Intelligence, Word Recognition, Linear Regression, Machine Learning
