基于多种算法融合的个性化定制陶瓷图像识别应用研究

Image Recognition of Customized Ceramics Based on Multi-algorithm Fusion

目前,在个性化定制陶瓷产品中,有着众多图像一致但局部文本不同的情况出现(例如:班级聚会合影留念的纪念瓷盘,仅是落款姓名不同),通过传统的图像识别技术难以进行有效识别。提出一种基于CTPN和CRNN两种深度学习模型的陶瓷图像中文本的识别方法,同时辅以传统SIFT算法,实现对无文本或文本识别难度高的图像识别。具体识别流程为,系统通过调用CTPN和CRNN模型,对其中的文本进行识别,并将文本信息与存储在数据库中的文本信息进行匹配并返回最佳结果。如果识别文本效果不佳,则利用SIFT算法提取出图像的特征点,并与库中的图像进行逐一对比,返回相似度较高的结果。通过研究实验测试,对于个性化定制陶瓷产品的图像识别,在性能上提升9倍以上,既解决了个性化定制陶瓷图像识别中识别耗时较长的问题,又解决了对只有局部文字不同的定制图像识别效果较弱的问题。

In personalized customized ceramic products,there are many cases where the image is the same but the partial text is different.For example,on the commemorative porcelain plates for a group photo at a class party,only the name of the signature is different.Therefore,it is difficult to effectively identify them by using the traditional image recognition technology.A text recognition method in ceramic images is proposed based on two deep learning models of CTPN and CRNN,supplemented with a traditional SIFT algorithm to realize image recognition in the case of no text or difficult text recognition.In the specific recognition process,the system recognizes the text by calling the CTPN and CRNN models,matches the text information with the text information stored in the database,and returns the best result.If the text recognition effect is not sufficient,the SIFT algorithm is used to extract the feature points of the picture,compare them with the pictures in the library one by one,and return a result with high similarity.Through experimental tests,the performance of image recognition for personalized customized ceramic products has been improved by more than 9 times.The problem of weak recognition effect of customized pictures is also addressed.