Identifing of Alphanumerical Codes in Promotional products by Using of Deep Neural Network

Çağrı Gider, S. Albayrak
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Abstract

Using the codes in promotional products was often considered a waste of time. For this reason most codes are not used and promotions do not show sufficient effectiveness. The theme of the project was to identify the promotional code on the product using artificial neural networks and deep learning methods. Bu projede, karakterlerin tanımlanması için, Keras ve Tensorflow kütüphaneleri kullanılarak Convolutional Neural Network (CNN) yapısında bir yapay sinir ağı kullanılmıştır. The project was created in the direction of the Optical Character Recognition application (OCR) requirement that emerged in a software for a customer company in adesso Turkey. The client company’s requirement is that a program to recognize characters with a special font. A mobile application has been implemented in the iOS environment to increase the efficiency and ease of implementation of the project. The OCR (Optical Character Recognition) library created in the project has been converted to Objective-C. Then the Objective-C library used in iOS program to use the Python model. In the system where the number of samples used in the training set is 7091, the model accuracy for 1200 test data is 99.7%.
利用深度神经网络识别促销商品中的字母数字代码
在促销产品中使用代码通常被认为是浪费时间。由于这个原因,大多数代码没有被使用,促销活动也没有显示出足够的效果。该项目的主题是使用人工神经网络和深度学习方法识别产品上的促销代码。但是projede, karakterlerin tanımlanması iin, Keras ve Tensorflow k t phaneleri kullanılarak卷积神经网络(CNN) yapısında bir yapay sinir ağı kullanılmıştır。该项目是根据光学字符识别应用程序(OCR)需求的方向创建的,该需求出现在土耳其阿德索的一家客户公司的软件中。客户公司的要求是一个程序来识别具有特殊字体的字符。在iOS环境中实现了一个移动应用程序,以提高项目的执行效率和易用性。项目中创建的OCR(光学字符识别)库已转换为Objective-C。然后用Objective-C库在iOS程序中使用Python模型。在训练集中使用的样本数为7091的系统中,1200个测试数据的模型准确率为99.7%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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