MACHINE VISION-BASED, DIGITAL DISPLAY INSTRUMENT POSITIONING AND RECOGNITION - Peng, Gang; Du, Bing; Li, Zhiyong; He, Dingxin (International Journal of Industrial Engineering: Theory, Applications and Practice, 2022)
Herein, an accurate and efficient algorithm for digital-display instrument positioning and recognition is proposed. The isolated forest algorithm and Otsu watershed threshold algorithm were used to distinguish digital-display instruments from nondigital-display instrument areas and separate the foreground from the background, respectively. The histogram of oriented gradient–support vector machine classification algorithm was used to distinguish instrument and non-instrument regions, which considerably improved the accuracy of digital-display instrument region positioning, avoided the interference of non-digital tube character regions, and reduced the search time of the digital tube region. A convolutional neural network was used for character recognition. Global characteristics of the character region were fully utilized, and partial digital character issues and scenarios where the decimal point is not obvious were mitigated. The proposed method can adapt to angle deviation, partial character missing, and image noise and exhibits excellent robustness and adaptability to the location and recognition of the digital tube.