  Print quality is an important criterion for a printer's performance. The
detection, classification, and assessment of printing defects can reflect the
printer's working status and help to locate mechanical problems inside. To
handle all these questions, an efficient algorithm is needed to replace the
traditionally visual checking method. In this paper, we focus on pages with
local defects including gray spots and solid spots. We propose a coarse-to-fine
method to detect local defects in a block-wise manner, and aggregate the
blockwise attributes to generate the feature vector of the whole test page for
a further ranking task. In the detection part, we first select candidate
regions by thresholding a single feature. Then more detailed features of
candidate blocks are calculated and sent to a decision tree that is previously
trained on our training dataset. The final result is given by the decision tree
model to control the false alarm rate while maintaining the required miss rate.
Our algorithm is proved to be effective in detecting and classifying local
defects compared with previous methods.
