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Recognition of Indonesian Sign Language Alphabets Using Fourier Descriptor Method

机译：使用傅立叶描述符方法识别印度尼西亚语言语言字母表

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Feature extraction is a process to search for special features of an object that will distinguish the characteristics between one object and another. In this study, the Fourier Descriptor method was used for extracting the feature of the Indonesian Sign Language (BISINDO) images. This research has been performed by implementing four main steps, i.e., pre-processing by converting RGB images to grayscale and binary ones, and closing operation for smoothing the images; contour detection using Moore's algorithm; feature extraction implementing Fourier descriptor with 5 kinds of coefficient i.e., 2, 5, 10, 25, and 50 to represent the feature of the images; and lastly feature recognition process by computing the image similarity using Euclidean Distance. 1820 images divided into 4 kinds i.e., standard, scaled, rotated, and translated images have been tested with 130 images of training data. Based on the test result of 130 standard images for each coefficient, the best accuracy is obtained at coefficient 25 and 50 with a similar accuracy of 96.92%. In addition, the recognition performance of Fourier descriptor and Euclidean distance reached up to above 72% in average for standard and scaled images.
机译：特征提取是一种用于搜索对象的特殊功能的过程，它将区分一个对象之间的特性和另一个对象之间的特征。在本研究中，傅立叶描述符方法用于提取印度尼西亚语言（Bisindo）图像的特征。通过实现四个主要步骤，即通过将RGB图像转换为灰度和二进制文件来进行预处理来执行该研究，以及关闭操作的关闭操作;使用MOORE算法的轮廓检测;特征提取实现具有5种系数I.，2,5,10,25和50的傅立叶描述符来表示图像的特征;并且通过使用欧几里德距离计算图像相似度来实现识别过程。 1820个图像分为4种，即标准，缩放，旋转和翻译图像已经使用130个训练数据进行了测试。基于每个系数的130个标准图像的测试结果，在系数25和50处获得最佳精度，其精度为96.92％。此外，傅立叶描述符的识别性能和欧几里德距离平均达到高于72％的标准和缩放图像。

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