Accurate and fast access to Vernier caliper readings is a critical issue in automated verification of Vernier calipers. To address this problem, this paper proposes a machine vision-based algorithm for reading the Vernier caliper’s displayed value. The suggested method first employs threshold segmentation and template matching to determine the region of interest and obtain the main ruler digit position by alternate projection. Then, we apply the improved LeNet5 network to identify the main ruler of the Vernier caliper, Moreover, we developed the first and last inscription method for reading the decimal part of the Vernier caliper and established our data set for model training. Extensive experiments on reading the displayed value have demonstrated our algorithm’s accuracy, which achieves a displayed value reading accuracy of 100%. Compared to other methods, the proposed technique affords better stability and accuracy.

Matched sampling is a methodology used to estimate treatment effects. A caliper mechanism is used to achieve better similarity among matched pairs. We investigate finite sample properties of matching with caliper and propose a slight modification to the existing mechanism. The simulation study compare performance of both methods and show that standard caliper perform well only in case of constant treatment or uniform propensity score distribution. Secondly, in a case of non-uniform distribution and non-uniform treatment the dynamic caliper method outperform standard caliper matching.