This website requires JavaScript.

CharNet: Generalized Approach for High-Complexity Character Classification

Boris Kriuk
Jan 2024
0被引用
0笔记
摘要原文
Handwritten character recognition (HCR) is a challenging problem for machine learning researchers. Unlike printed text data, handwritten character datasets have more variation due to human-introduced bias. With numerous unique character classes present, some data, such as Logographic Scripts or Sino-Korean character sequences, bring new complications to the HCR problem. The classification task on such datasets requires the model to learn high-complexity details of the images that share similar features. With recent advances in computational resource availability and further computer vision theory development, some research teams have effectively addressed the arising challenges. Although known for achieving high efficiency, many common approaches are still not generalizable and use dataset-specific solutions to achieve better results. Due to complex structure and high computing demands, existing methods frequently prevent the solutions from gaining popularity. This paper proposes a straightforward, generalizable, and highly effective approach (CharNet) for detailed character image classification and compares its performance to that of existing approaches.
展开全部
机器翻译
AI理解论文&经典十问
图表提取
参考文献
发布时间 · 被引用数 · 默认排序
被引用
发布时间 · 被引用数 · 默认排序
社区问答