利用机器学习和OCR验证技术识别基于可见包装特征的产品类别

Takorn Prexawanprasut, Lalita Santiworarak, Piyaporn Nurarak, Poom Juasiripukdee
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引用次数: 0

摘要

清关是国际贸易领域必须完成的一个具有挑战性和耗时的过程。结果,货物经常在港口延误。如果工作人员知道物品的初始数量,即使他们不在现场,他们也可以继续进行其他程序。图像处理在这方面很有帮助,因为它允许根据包装的外观预测商品的类型。这样就可以在员工到达现场之前确定每种产品的数量。三家不同的进出口公司提供了5675张照片,并使用机器学习方法创建了一个模型,该模型可以预测属于五类之一的事物类型。此外,研究人员还开发了一种基于ocr的分类算法,目的是让机器学习更好地处理某些难以学习的事物。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Employing Machine Learning and an OCR Validation Technique to Identify Product Category Based on Visible Packaging Features
Customs clearance is a challenging and time-consuming process that must be completed in the sphere of international trade. As a result, the cargo is frequently delayed at the port. If the personnel know the initial number of items, they may be able to continue with other procedures even when they are not physically present at the location. Image processing is helpful in this area since it allows for the prediction of the type of goods based on the appearance of the package. This allows for the determination of the quantity of each type of product prior to the arrival of the employees at the site. Three distinct import-export companies contributed 5,675 photos, and a machine learning approach was used to create a model that can predict the types of things that fall into one of five categories. Also, the researchers made an OCR-based classification algorithm with the goal of making machine learning work better for certain types of things that have trouble learning.
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