基于深度学习的手写汉字美感评分
首发时间:2019-01-28
摘要:为帮助青少年儿童提高汉字书写质量,更好地辅助汉字书写教学,本文探究了手写汉字美感评分的有关技术,结合当前最新的深度学习理论思想,提出了基于相似度检索策略的手写汉字美感评分方法,根据汉字和美感分数两个条件划分了相似度检索的不同类别,并创新性地将手写汉字深度学习网络提取到的CNN特征与传统的结构特征结合起来,提高手写汉字美感评分的准确率。同时,针对本课题的应用场景和模型训练需求,本文收集整理了带有美感分数的手写汉字图片数据集--小学宝练字作品数据集,利用该数据集进行模型训练和实验测试。实验结果表明,本文的模型和方法对手写汉字的美感评分具有较高的有效性和实用价值。
关键词: 人工智能 深度学习 卷积神经网络 手写汉字美感评分 相似度检索
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HANDWRITTEN CHINESE CHARACTER AESTHETIC GRADING BASED ON DEEP LEARNING
Abstract:In order to help young children improve the quality of Chinese writing and assist Chinese character writing teaching better, this paper explored the related techniques of handwritten Chinese character aesthetic grading. Combined with the latest deep learning theory, this paper proposed a handwritten Chinese character aesthetic grading method based on similarity retrieval strategy. According to the two conditions of Chinese characters and aesthetic scores, the different categories of similarity search are divided. The CNN features extracted from the handwritten Chinese character deep learning network are innovatively combined with the traditional structural features to improve the accuracy of handwritten Chinese characters grading. For the application scenarios and model training needs of this project, this paper collected the handwritten Chinese character image data set with the aesthetic scores - the XiaoXueBao data set for model training and experimental testing. The experimental results showed that the model and method of this paper have high effectiveness and practical value for the aesthetic grading of handwritten Chinese characters.
Keywords: Artificial intelligence deep learning convolutional neural network handwritten Chinese character aesthetic grading similarity search
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