Department of Electrical and Computer Engineering, University of Dayton, Dayton, OH, USA.
Department of Earth and Atmospheric Sciences, Saint Louis University, St. Louis, MO, USA.
Comput Intell Neurosci. 2018 Aug 27;2018:6747098. doi: 10.1155/2018/6747098. eCollection 2018.
In spite of advances in object recognition technology, handwritten Bangla character recognition (HBCR) remains largely unsolved due to the presence of many ambiguous handwritten characters and excessively cursive Bangla handwritings. Even many advanced existing methods do not lead to satisfactory performance in practice that related to HBCR. In this paper, a set of the state-of-the-art deep convolutional neural networks (DCNNs) is discussed and their performance on the application of HBCR is systematically evaluated. The main advantage of DCNN approaches is that they can extract discriminative features from raw data and represent them with a high degree of invariance to object distortions. The experimental results show the superior performance of DCNN models compared with the other popular object recognition approaches, which implies DCNN can be a good candidate for building an automatic HBCR system for practical applications.
尽管在目标识别技术方面取得了进展，但由于存在许多模棱两可的手写字符和过度草书的孟加拉语手写体，手写孟加拉语字符识别 (HBCR) 仍然在很大程度上未得到解决。即使许多先进的现有方法在实际涉及 HBCR 的情况下也无法达到令人满意的性能。在本文中，讨论了一组最先进的深度卷积神经网络 (DCNN)，并系统地评估了它们在 HBCR 应用中的性能。DCNN 方法的主要优点是它们可以从原始数据中提取鉴别特征，并以高度不变性表示对象变形。实验结果表明，与其他流行的目标识别方法相比，DCNN 模型具有优越的性能，这意味着 DCNN 可以成为构建用于实际应用的自动 HBCR 系统的良好候选者。
