A Parallel Text Recognition in Electrical Equipment Nameplate Images Based on Apache Flink

Abstract

Information on the equipment nameplate is important for the storage, transportation, verification and maintenance of electrical equipment. However, because a natural image of the device on the text nameplate may be multidirectional, curved, noisy or blurry, automatically recognizing the image from the device nameplate can be difficult. Meanwhile, image preprocessing methods are carried out in a serial manner, so the processing speed with regard to the above problems is slower and takes a longer time. Accordingly, this study proposes a parallel and deep-learning-based text automatic recognition method. In the proposed method, a pretreatment method comprising edge detection, morphological manipulation and projection transformation is used to obtain the corrected nameplate region. The connectionist text proposal network (CTPN) is then activated to detect text lines on the corrected nameplate area. Next, a deep-learning method is proposed to study the classification methods of convolutional recurrent neural networks and connectionist time classification for identifying text in each line of text detected by CTPN. Finally, we use Apache Flink to parallelize the above processes, including parallelization preprocessing and bidirectional long short-term memory parallelization in the process of text line detection and text recognition. Experimental results on the collected nameplate show that the proposed imaging processing method has a good recognition performance and that the parallelization method significantly reduces the data processing time cost.