Abstract: Aiming at the calibration of the electric meter and the test work in extreme environments， it is still necessary to manually detect whether the electric meter has internal component fault or errors. A research method of electric meter digital recognition based on image threshold optimization and improved SVM is proposed. First， we use edge search to obtain the display area of the image， use adaptive threshold for binarization， and then perform a series of filtering processing on the image， and then further extract the image of a single number， combined with image threshold optimization， before retaining the digital image. On the premise of eigenvalues， the redundant eigenvalues are removed， and the display area image is divided into several single digital images. Finally， based on the improved SVM multi-class recognition model， each digit from 0 to 9 is trained， and the trained model is used to identify the single digit image in turn. The experimental results show that compared with the classical convolutional neural network model for the recognition of LED liquid crystal digits， the optimization and improvement of the SVM model based on the image threshold have faster recognition speed and higher accuracy.

Key words: electricity meter, threshold optimization, support vector machines, digital identification