Engraved digit detection using HOG–real AdaBoost and deep neural network

This paper proposes a framework for recognizing sequences of digits engraved on steel plates. These digits are normally blurred, dirty, not clear, tilted, and sometimes overlapped by other digits. Several digits in a string with uneven spacing and different sizes are detected at the same time. The framework consists of two main components called histogram of oriented gradient-real AdaBoost module and deep neural network module. The first component is used to detect digit windows, and the second component is employed to recognize digits inside the detected windows. Experimental results demonstrated that the proposed framework could be a potential solution to recognize the engraved digits.