A Survey of Methods and Techniques in Offline Telugu Character Segmentation and Recognition

Abstract

AbstractTelugu is one of South India’s oldest languages. It has a complicated orthography with various different character shapes. For so many decades, offline character recognition has always been a popular area of study. Handwriting segmentation and recognition are difficult, which has stimulated the interest of industry and academic researchers. Methods for recognizing handwriting have become much more prominent in recent years. Until now, the connected component approach, vertical and horizontal profiles, and other approaches were presented in the literature for the segmentation of printed character's work. The selection of the best discriminative features is an important concern in character recognition. Various statistical and structural features, as well as various combinations of them, are discussed in the research work. Researchers used ANN, SVM, KNN, and CNN to classify offline Telugu characters. The purpose of this article is to closely analyze various feature extraction methods and classification models to understand the problem and challenges encountered by earlier research. This identification is meant to provide numerous recommendations for advancements and scope.Keywordsk-nearest neighbor (KNN)Support vector machines (SVM)Convolution neural networks (CNN)Optical character recognition (OCR)Character recognition (CR)Natural language processing (NLP)Connected component (CC)Histogram of oriented gradients (HOG)Fast Fourier transform (FFT)Discrete cosine transform (DCT)Discrete wavelet transform (DWT)Principal component analysis (PCA)Fourier transform (FT)Particle swarm optimization (PSO)Differential evolution (DE)