Data normalization for triangle features by adapting triangle nature for better classification

Geometry features especially triangle has been widely used in face, fingerprint, vehicle detection and digit recognition. Features from the triangle are used to generate useful features for classification processed. Recently, triangle features used in digit recognition has adopted angle as part of features. This has influenced accuracy due to big gap between angle values and other feature values such as ratio and gradient of sides. To overcome this issue, data normalization can be used to address the issue. Experiments have been made using existing normalization techniques such as Z-score, Minimax and libSVM scale function. Experiments have been conducted using Z-Score and libSVM scale function, but results of classification are worst compared to triangle features without normalization. Thus, the results of classification can be improved by proposed a new technique of normalization based on nature of triangle geometry. In this paper, we have proposed a new normalization technique by adopting the nature of triangle geometry. Datasets HODA, MNIST, IFHCDB and BANGLA digit have been chosen to extract triangle features. Then, we will apply normalization on the extracted features before classify them by using Support Vector Machine. The results shows normalization by adapting the nature of triangle geometry gives better result compared to other techniques. The proposed normalization technique only applies to Cartesian Plane Zone that contributes 45 features. The benchmarking for other researchers should refer to our 25 zones that give 225 features of triangle geometry.