Metallic debossed characters industrial online non-segmentation identification based on improved multi-scale image fusion enhancement and deep neural network

This paper investigates accurate industry online scene text recognition techniques for metallic debossed characters (MDCs). As MDCs have low contrast with its background and easy to interfere with corrosion rust, oxidate skin or specular reflection and so forth, a multi-scale image fusion algorithm is first proposed to restore the MDCs' shape and appearance information to enhance the sample's contrast by using the MDCs' depth characteristic with the help of four-directional illumination. With this method, the three most important reflection-related picture quality evaluation standards, namely contrast, saturation, and exposure, can be enhanced simultaneously. Next, based on the fusion result, a U-shaped network based on VGG-16 network architecture is used for text localization. In order to improve network receptive fields, an improved post-processing output module that combines a text sequence score map for coarsening localization with a single-character score map for fine localization is proposed, so that the network's adaptability and accuracy for long texts are improved. Finally, a convolutional recurrent neural network is adopted to realize the recognition of sequence characters. The advantages and effectiveness of the proposed method are statistically analyzed with the data from a liquefied petroleum gas cylinder annual periodic inspection production line.

Automated summary

This study investigates accurate industry online scene text recognition techniques for metallic debossed characters (MDCs) by proposing a multi-scale image fusion algorithm and utilizing a U-shaped network for text localization. The methods enhance the contrast and accuracy of MDC recognition for long texts.