Tri-stage offline Telugu character recognition system based on fusion of HOG and ULBP

Abstract

The domain of pattern recognition has experienced a notable upsurge in the prevalence of handwritten character recognition, which is a research area that has been widely recognized for decades.However, this task presents significant challenges, especially for Indian languages, due to the vast array of character shapes and curved linear structures they possess. Despite some research having been conducted on Indian languages such as Kannada, Hindi, Tamil Devanagari, and Telugu, among others, the field is still in its nascent stages of development. The nascent phase of handwritten Telugu script presents a promising opportunity for further investigation and exploration. We introduce a Tri-stage Telugu handwritten recognition system that utilizes hyper parameters of k- NN, SVM, and Random Forest based on the fusion of HOG and ULBP features. By implementing a Tri-stage classifier in a sequential manner, the system's recognition accuracy is significantly enhanced.The proposed Tri-stage classifier achieves exceptionally high accuracies on Telugu handwritten character HP datasets.

Keywords

• Handwritten character recognition(HWR)
• Histograms of oriented gradients (HOG)
• Uniform local binary pattern (ULBP)
• Support vector machine (SVM)
• K-nearest neighbor (k-NN)
• Random forest(RF)Classifier