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CharNet: Generalized Approach for High-Complexity Character Classification. (arXiv:2401.17098v1 [cs.CV])
cs.CV updates on arXiv.org arxiv.org
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 …
arxiv bias character recognition classification complexity cs.cv data datasets generalized human machine machine learning recognition researchers scripts text variation