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Multi-Language Handwritten Digits Recognition based on Novel Structural Features

机译：基于新颖结构特征的多语言手写数字识别

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摘要

Automated handwritten script recognition is an important task for several applications. In this article, a multi-language handwritten numeral recognition system is proposed using novel structural features. A total of 65 local structural features are extracted and several classifiers are used for testing numeral recognition. Random Forest was found to achieve the best results with an average recognition of 96.73%. The proposed method is tested on six different popular languages, including Arabic Western, Arabic Eastern, Persian, Urdu, Devanagari, and Bangla. In recent studies, single language digits or multiple languages with digits that resemble each other are targeted. In this study, the digits in the languages chosen do not resemble each other. Yet using the novel feature extraction method a high recognition accuracy rate is achieved. Experiments are performed on well-known available datasets of each language. A dataset for Urdu language is also developed in this study and introduced as PMU-UD. Results indicate that the proposed method gives high recognition accuracy as compared to other methods. Low error rates and low confusion rates were also observed using the novel method proposed in this study. (C) 2019 Society for Imaging Science and Technology.%020502.1-020502.10
机译：自动手写脚本识别对于一些应用程序来说是一项重要任务。在本文中，提出了一种具有新颖结构特征的多语言手写数字识别系统。总共提取了65个局部结构特征，并使用多个分类器测试数字识别。发现随机森林取得了最佳结果，平均识别率为96.73％。该方法已在六种不同的流行语言上进行了测试，包括阿拉伯文西文，阿拉伯文东文，波斯文，乌尔都文，梵文和孟加拉文。在最近的研究中，目标是单个语言数字或具有相似数字的多种语言。在这项研究中，所选语言中的数字彼此不相似。然而，使用新颖的特征提取方法，可以实现较高的识别准确率。在每种语言的已知可用数据集上进行实验。这项研究中还开发了乌尔都语语言的数据集，并将其作为PMU-UD引入。结果表明，与其他方法相比，该方法具有较高的识别精度。使用本研究中提出的新方法还观察到了低错误率和低混乱率。 （C）2019影像科学与技术学会％020502.1-020502.10

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  • 来源
    《Journal of Imaging Science and Technology》 |2019年第2期|28-37|共10页
  • 作者

    Alghazo Jaafar M.; Latif Ghazanfar; Alzubaidi Loay; Elhassan Ammar;

  • 作者单位

    Prince Mohammad Bin Fand Univ, Coll Comp Engn & Sci, Khobar, Saudi Arabia;

    Prince Mohammad Bin Fand Univ, Coll Comp Engn & Sci, Khobar, Saudi Arabia|Univ Malaysia, Fac Comp Sci & Informat Technol, Sarawak, Malaysia;

    Prince Mohammad Bin Fand Univ, Coll Comp Engn & Sci, Khobar, Saudi Arabia;

    Princess Sumaya Univ Technol, King Hussein Sch Comp Sci, Amman, Jordan;

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