Pengenalan Tulisan Tangan Angka menggunakan Self Organizing Maps (SOM)
Pengenalan Tulisan Tangan Angka menggunakan Self Organizing Maps (SOM)
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Pengenalan Tulisan Tangan Angka menggunakan 自组织图 (SOM)
DOI：
10.47065/bits.v3i1.1002
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Gita Fadila Fitriana
中科院分区：
文献类型：
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作者：
Gita Fadila Fitriana
手写是一种字符模式识别。字符模式识别是一项令人兴奋的研究。在字符模式识别中，许多类型的字符都可以被计算机识别，并可以用各种算法来解决。各种模式识别技术已经成功地应用于语音识别、人脸检测、指纹识别、手写识别等多个领域。笔迹识别分为两类，即在线笔迹识别和离线笔迹识别。在线笔迹识别需要特殊的电子设备，笔迹是在压敏平板电脑上捕捉的。脱机手写识别不需要特定的机器，因为手写数据是从先前写入的文本输入的，例如扫描仪扫描的图像。已经开发了几种方法来以不同程度的准确度识别笔迹。本研究采用联合矩不变(UMI)和自组织映射(SOM)两种方法进行特征提取。基于整个测试数据集的软件实验结果，主数据对50幅图像的准确率为88%，第一次数据对500幅图像的准确率为98.2%。然而，对于50个测试数据的第二次数据实验，准确率为90%。第三次数据实验为500个测试数据。准确率为89%。当从精确值来看，与具有不同量的两个次要数据相比，主要数据的精确度水平较低。用不同的数据集进行实验所产生的准确性证明，手写字符的变化程度很高，而且不一致。这是由于每个人笔迹的厚度和形式并不一致，以及影响笔迹特征的习惯造成的。主数据是直接通过扫描仪过程获取的数据，在手写的数字图像中仍然存在大量噪声。同时，对二次数据进行了灰度图像处理，使手写图像无噪声。
Handwriting is character pattern recognition. Character pattern recognition is exciting to do research. In character pattern recognition, many types of characters can be recognized by computers and solved by various algorithms. Various kinds of pattern recognition have been successfully applied in multiple fields such as voice recognition, face detection, fingerprint recognition, and handwriting recognition. Handwriting recognition is divided into two types, namely online handwriting recognition and offline handwriting recognition. Online handwriting recognition requires special electronic equipment, and handwriting is captured on a pressure-sensitive tablet. Offline handwriting recognition does not need a particular machine because handwriting data is entered from previously written text such as images scanned by a scanner. Several methods have been developed to recognize handwriting with varying degrees of accuracy. This research uses the feature extraction of United Moment Invariant (UMI) and Self Organizing Maps (SOM). Based on the results of the software experiment for the entire test data set, the primary data yielded an accuracy rate of 88% for 50 images, and the first secondary data paid an accuracy rate of 98.2% for 500 images. However, for the second secondary data experiment with 50 test data, the accuracy rate is 90%. The third secondary data experiment was 500 test data. The accuracy rate was 89%. When viewed from the accuracy value, the primary data has a lower level of accuracy when compared to the two secondary data with different amounts. The story of accuracy resulting from experimenting with varying data sets proves that handwritten characters have a high and inconsistent level of variation. This is caused by the thickness and form of writing that is not consistent in each person and habits that affect the character of one's handwriting. Primary data is data that is taken directly and through a scanner process and still has a lot of noise in the handwritten image of numbers. At the same time, the secondary data has undergone a grey image process so that the handwritten image is clean from noise.