An Efficient Document Skew Detection Method Using Probability Model and Q Test

Abstract

Document skew detection is one of the key technologies in most of the document analysis systems. However, existing skew detection methods either have low accuracy or require a large amount of computation. To achieve a good tradeoff between efficiency and performance, we propose a novel skew detection approach based on bounding boxes, probability model, and Dixon’s Q test. Firstly, bounding boxes are used to pick out the eligible connected components (ECC). Then, we calculate the slopes of the skew document with the probability model. Finally, we find the optimal result with Dixon’s Q test and projection profile method. Moreover, the proposed method can detect the skew angle in a wider range. The experimental results show that our skew detection algorithm can achieve high speed and accuracy simultaneously compared with existing algorithms.

IoT-Based Small Scale Anomaly Detection Using Dixon’s Q Test for e-Health Data

“…Now, to find the probability density of r on the Jacobian, we can integrate −∞ < x < ∞ and 0 ≤ v < ∞. Equation (11) shows the Jacobian evolved probability distribution of r. L and M present the variables as given in Equations ( 12) and ( 13), respectively.…”

“…However, due to lack of hardware resources such as processor and memory, an IoT device may face severe difficulty [10]. Further, depicting a point anomaly from a very small-size dataset makes the whole process questionable [11,12].…”