计算机科学
编码器
表达式(计算机科学)
趋同(经济学)
字错误率
人工智能
系列(地层学)
国家(计算机科学)
算法
模式识别(心理学)
机器学习
经济增长
生物
操作系统
古生物学
经济
程序设计语言
作者
Aniket Pal,Krishna Pratap Singh
标识
DOI:10.1016/j.asoc.2023.110457
摘要
Handwritten Mathematical Expression Recognition (HMER) is essential to online education and scientific research. However, discerning the length and characters of handwritten mathematical expressions remains a formidable challenge. In this study, we propose an encoder–decoder architecture incorporating a novel Adaptive momentum-based Regularized Gated Recurrent Unit (AdamR-GRU) to solve this problem. It incorporates a novel gate within the GRU framework to maintain input histories, enabling extended memory retention and faster convergence. To assess the performance of AdamR-GRU, we conducted a series of comprehensive experiments on the CROHME 2014, 2016, and 2019 datasets. The results indicate that the AdamR-GRU model exhibits competitive performance with existing state-of-the-art methods regarding Expression Recognition Rate (exprate). Moreover, our model achieves state-of-the-art accuracy concerning 1, 2, and 3 symbol-level errors (top 1, 2, 3 error accuracy) on the CROHME 2014 and 2016 datasets. For the CROHME 2019 dataset, the model attains state-of-the-art performance in top 2 and 3 error accuracy. AdamR-GRU demonstrates reduced computational complexity and memory usage compared to alternative contemporary models.
科研通智能强力驱动
Strongly Powered by AbleSci AI