Small-Scale Robust Digital Recognition of Meters Under Unstable and Complex Conditions

Digital recognition of meters aims to identify numbers in complex environments. Existing methods of digital recognition of meters are dependent on deep networks supported by high-quality large-scale data and features extraction, whereas low-quality small-scale digital datasets are not effective in recognition. Moreover, occlusion of digital images obstructs the extraction of fixed features. Multi-classifier under Feature Engineering (MC-FE) is proposed to solve the above problems. To be specific, MC-FE builds a feature library containing a variety of mainstream features. It directly selects the optimal combination of distinguishing features applicable to the current dataset, rather than using the fixed features. In addition, 10 regression machines integrated by support vector machines are adopted. The respective regression machine determines the probability of every number from 0 to 9. The positioning of the meter is the premise of accurate identification. A multi-layer kernel regression positioning (ML-KRP) is designed to increase the accuracy of meter identification. The results of the experiments on several digital recognition datasets reveal that MC- FE and ML-KRP outperform the state-of-the-arts in digital recognition under this small-scale setting.