Due to constant advancement of computer tools, automated conversion of images of typed,handwritten and printed text is important for various applications, which has led to intense research for several years in the field of offline handwritten character recognition. Handwritten character recognition is complex because characters differ by writing style, shapes and writing devices. To resolve this problem, we propose a fuzzy-based multi-kernel spherical support vector machine. Initially, the input image is fed into the pre-processing step to acquire suitable images. Then, histogram of oriented gradient (HOG) descriptor is utilised forfeature extraction. The HOG descriptor constitutes a histogram estimation and normalisation computation. The features are then classified using the proposed classifier for character recognition. In the proposed classifier, we design a new multi-kernel function based on the fuzzy triangular membership function. Finally, a newly developed multi-kernel function is incorporated into the spherical support vector machine to enhance the performance significantly. The experimental results are evaluated and performance is analysed by metrics such as false acceptance rate, false rejection rate and accuracy, which is implemented in MATLAB. Then, the performance is compared with existing systems based on the percentage of training data samples. Thus, the outcome of our proposed system attains 99% higher accuracy, which ensures efficient recognition performance