Weighted DCT coefficient based text detection
Su Lu, Kenneth E. Barner · IEEE International Conference on Acoustics Speech and Signal Processing · 2008
This paper addresses text detection utilizing weighted discrete cosine transform (DCT) coefficients as a discrimination statistic. The sum of absolute value of DCT coefficients in each block is considered as an energy statistic for detection. Linear discriminant analysis (LDA) is conducted to calculate the optimal threshold. Different types of weights are employed to strengthen discrimination statistic, which include uniform, binary, linear and quadratic weights. The evaluation of weighted algorithms is conducted in the receiver operating characteristics (ROC) space. The ROC curves show that there is a tradeoff between true positive rate (TPR) and false positive rate (FPR) for all weight configurations. In terms of maximizing the separation between two distributions, experimental results show that the quadratic weighted energy achieves the best recall and precision.