Abstract: The existing deep learning-based water meter reading recognition methods generally recognize each digit or pointer of the water meter in isolation,and then simply splices the recognition results of each bit for the final result.However,due to the existence of occlusal gaps between the counting gears of the water meter,possible structural errors in the water meter itself,and the shooting angle,there may be situations such as incomplete display of the water meter word wheel digits,the word wheel turning between two digits,and deviation of the pointer indication,etc.,at which time,a simple combination of the recognition results of each bit of the digits or pointers may lead to errors in the final recognition results.To address the above problems,this paper proposes a water meter reading recognition method based on deep learning and a priori correction.The method is based on the PaddlePaddle framework,uses the lightweight model architecture MobileNetV3 and SVTR to read the word wheel region,and at the same time,uses the image processing technology to read the pointer reading.Finally,it takes full advantage of the correlation a priori knowledge of the correlation between each digit in the word wheel of the water meter and the readings of each pointer to correct the recognition results.In this paper,the recognition and correction methods of the word wheel area and the pointer area are discussed.These methods are applied to the water meter images for experimental testing,and compared with the existing methods.The results show that the proposed method can effectively improve the accuracy of the water meter reading recognition results.

Key words: Deep learning, Image processing, Text detection, Text recognition, Pointer recognition, Optical character recognition