An Approach for Character Recognition in Piston Cavity with Faster R-CNN and Prior Knowledge Library of Character Sequences

Abstract

In the manufacturing process of piston, most of the piston cavities are printed with different character sequences to describe the specifications of piston. It is labor-consuming and inefficient to read the piston cavity character sequences manually. Although scholars have done a lot of researches in the field of industrial character recognition, there are few researches on piston cavity character recognition. A piston cavity character recognition method based on Faster R-CNN and priori knowledge library of character sequences is presented. First, we design a ring light source and an imaging device for the piston cavity based on the characters of the piston cavity protruding upward and the texture of the metal being easily reflective. Second, we use the character images in piston cavity collected by the imaging device to make character dataset. Third, according to the noisy background of the piston cavity image, Gaussian filtering and morphological operations were used to obtain a clean background image of the piston cavity. Fourth, use the Faster R-CNN training dataset to get the character recognition model, and then use the character type and position information detected by the recognition model to form a character sequence. Fifth, according to the highly similar characteristics of the character sequences of the same piston model, a character sequences prior knowledge library is constructed to correct the recognition results of Faster R-CNN. The experimental results show that the accuracy rate of the character sequences detected by the character recognition model is 95.5%. And when combined with the prior library of character sequences, the accuracy rate of the character sequences is 99%.