Handwritten digit recognition has got a special role in different applications in the field of digital recognition including; handwritten address detection, check, and document. Persian handwritten digits classification has been facing difficulties due to different handwritten styles, inter-class similarities, and intra-class differences. In this paper, a novel method for detecting Persian handwritten digits is presented. In the proposed method, a combination of Histogram of Oriented Gradients (HOG), 4-side profiles of the digit image, and some horizontal and vertical samples was used and the dimension of the feature vector was reduced using Principal Component Analysis (PCA). The proposed method applied to the HODA database, and Support vector Machine (SVM) was used in the classification step. Results revealed that the detection accuracy of such method has 99% accuracy with an adequate rate due to existing unacceptable samples in the database, therefore, the proposed method could improve the outcomes compared to other existing methods.

Establishment of rating curves are often required by the hydrologists for flow estimates in the streams, rivers etc. Measurement of discharge in a river is a time-consuming, expensive, and difficult process, and the conventional approach of regression analysis of stage-discharge relation does not provide encouraging results especially during the floods. Present study is aimed at the application of Support vector machines (SVMs) based algorithm for modeling stage-discharge relation including the hysteresis effect. A data set of two discharge-measuring stations located on two Indian rivers has been used for analysis in the present study. A back propagation neural network model was employed in order to compare the performance of the results based on Support vector machines based modeling technique. The outcome of the study suggests that the Support vector machines works well for both the data sets and produce promising results in comparison to the neural network technique. Finally, the results also suggest the suitability of SVMs algorithm in predicting the looped rating curve having hysteresis effect.