# Blockwise Based Detection of Local Defects

## TL;DR

This paper introduces a blockwise, coarse-to-fine algorithm for detecting and classifying local printing defects like gray and solid spots, improving accuracy over previous methods.

## Contribution

It presents a novel block-wise detection approach with a decision tree classifier for local printing defect identification.

## Key findings

- Effective detection and classification of local defects.
- Improved false alarm control while maintaining low miss rate.
- Superior performance compared to previous methods.

## Abstract

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.