A new method for recognizing digital numbers on coal gas meters

In this paper a new algorithm for recognizing digital numbers on coal gas meters is presented. Firstly, the RGB image of the coal gas meter is converted into the CIE 1931 XYZ color space, and the digital area is extracted in the CIE space by four line equations. Secondly the horizontal and vertical edges are detected on the G value image of the original RGB image, and tilt correction is finished by using the Radon transformation algorithm. Thirdly the CMYK color features are used to strengthen the contrast, and the digital regional characteristics are enhanced by using morphological operations. Fourthly, the ideal binary image is obtained by combing the `Otsu method´ with a ratio estimation method, and then row and column projections are done to finish the segmentation of the digital numbers. Finally a neural network model trained using the back-propagation algorithm is adopted to recognize the numbers. Experimental results show that our method can attain ideal recognition results.