Recognition of Laser-Printed Characters Based on Creation of New Laser-Printed Characters Datasets
Recognition of Laser-Printed Characters Based on Creation of New Laser-Printed Characters Datasets
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基于新激光打印字符数据集创建的激光打印字符识别
DOI：
10.1007/978-3-030-86198-8_29
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
TAKESHI FURUKAWA
中科院分区：
文献类型：
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作者：
高塚;木原，日野，黒田，関口，岡崎;TAKESHI FURUKAWA
本文报道了一种激光打印字符识别的新方法。我们创建新的数据集来确认我们方法的准确性。在创建我们的数据集时，打印计数是受控的，并且打印的字符类型是标准化的。我们购买了8台全新的制造商激光打印机，并在每个制造商品牌上打印了1000张纸。我们从1000张印刷品中选择了第1、2、299、300、499、500、799、800、999和1000页。每张纸上的100个印刷字符用显微镜连接的高分辨率CCD相机捕获，并排列到数据集中。我们提取数据集中人物图像的轮廓，并在轮廓上跟踪x和y坐标。将每个坐标视为一个波，用八尺度小波分解进行分析。我们根据分解结果在八个品牌的激光打印机之间进行识别。在学习阶段，学习了印在每张纸上的200个“a”和44个“e”字符，在测试阶段，测试了印在同一张纸上的800个“a”和400个“e”字符的剩余部分。结果表明，基于子空间的方法识别的错误率为2.78。此外，使用支持向量机识别的ER为5.67，使用卷积神经网络识别的ER为2.56。因此，我们确认我们的方法能够使用我们的数据集识别激光打印字符的制造商和品牌。
We report a new method for the recognition of laser-printed characters. We create new datasets to confirm the accuracy of our methods. When creating our datasets, printing counts are controlled, and printed character type is standardized. We purchase eight brand new manufactures laser printers and printed characters on one thousand paper sheets at each manufacture brand. We chose pages 1, 2, 299, 300, 499, 500, 799, 800, 999, and 1000 from one thousand printed sheets. One hundred printed characters on each paper sheet were captured with a high-resolution CCD camera attached with a microscope and are arranged to datasets. We extracted contours of the characters’ images of the datasets and traced x and y coordinates on the contours. Each of the coordinate was considered as a wave and was analyzed with eighth scale wavelet decomposition. We recognize between the eight brands of laser printers based on the decomposition result. In the learning phase, two hundred numbers of ‘a’ and forty four numbers ‘e’ characters printed on each sheet was leaned while in the testing phase remains of eight hundred numbers of ‘a’ and forty hundred ‘e’ characters printed on the same sheets are tested. The result shows the error rate (ER) of recognition is 2.78using the subspace-based method. In addition, the ER of recognition is 5.67using a support vector machine and 2.56using a convolutional neural network. As a result, we confirm that our method is able to recognize manufacturers and brands of the laser-printed characters using our datasets.