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众多应用，例如盲人阅读工具和手写银行支票的阅读工具，一直是模式识别中一个令人着迷的研究领域。因此，各种应用程序和系统都需要将笔迹正确和准确地转换为计算机算法可以轻松识别和处理的有组织的数字文件。本文提出了一种准确而精确的手写识别自主结构，使用 ShuffleNet 卷积神经网络对离线手写字符和数字进行多类识别。开发的系统利用强大的 ShuffleNet CNN 的迁移学习来训练、验证、识别、并将手写字符/数字图像数据集分类为26个英文字符类和10个数字字符类。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%，优于现有技术中报告的其他对比字符识别系统。此外，已观察到所提出的模型的计算成本较低，单样本推理的平均时间为 2.7 (ms)。