Department of Computer Science/Cybersecurity, Princess Sumaya University for Technology, Amman, Jordan.
Gene Expr Patterns. 2022 Sep;45:119263. doi: 10.1016/j.gep.2022.119263. Epub 2022 Jul 16.
Handwritten character recognition has continually been a fascinating field of study in pattern recognition due to its numerous real-life applications, such as the reading tools for blind people and the reading tools for handwritten bank cheques. Therefore, the proper and accurate conversion of handwriting into organized digital files that can be easily recognized and processed by computer algorithms is required for various applications and systems. This paper proposes an accurate and precise autonomous structure for handwriting recognition using a ShuffleNet convolutional neural network to produce a multi-class recognition for the offline handwritten characters and numbers. The developed system utilizes the transfer learning of the powerful ShuffleNet CNN to train, validate, recognize, and categorize the handwritten character/digit images dataset into 26 classes for the English characters and ten categories for the digit characters. The experimental outcomes exhibited that the proposed recognition system achieves extraordinary overall recognition accuracy peaking at 99.50% outperforming other contrasted character recognition systems reported in the state-of-art. Besides, a low computational cost has been observed for the proposed model recording an average of 2.7 (ms) for the single sample inferencing.
手写字符识别由于其在现实生活中的诸多应用，如盲人阅读工具和手写银行支票阅读工具，一直是模式识别领域中一个引人入胜的研究领域。因此，需要将手写体正确、准确地转换为可被计算机算法轻松识别和处理的有组织的数字文件，以便于各种应用和系统使用。本文提出了一种使用 ShuffleNet 卷积神经网络的精确和准确的自主结构，用于生成离线手写字符和数字的多类识别。所开发的系统利用强大的 ShuffleNet CNN 的迁移学习来训练、验证、识别和分类手写字符/数字图像数据集，将其分为 26 类英语字符和 10 类数字字符。实验结果表明，所提出的识别系统实现了非凡的整体识别准确率，最高可达 99.50%，优于现有的其他字符识别系统。此外，所提出的模型的计算成本较低，单个样本推断的平均时间为 2.7（ms）。
