<p>Devanagari is one of the most widely recognized scripts in India and is used to write several languages, including Hindi, Marathi, Nepali, and Sanskrit. This paper presents a methodology for evaluating writers based on their offline handwritten Hindi characters. Assessing writers by their handwriting styles is challenging due to the unique styles of different individuals. The proposed technique offers a grading system that can be effectively used to judge Hindi handwriting competitions and determine winners using this methodology. The handwriting evaluation system involves several stages: pre-processing, feature extraction, classification, and grading based on classification scores. This study considers the features of individual characters and employs a k-nearest neighbour classifier with Euclidean distance to calculate the classification scores for each writer. For the training dataset, four printed Devanagari fonts—Devlys, Krishna, Krutidev, and Utsaah—were used. For the testing dataset, samples of handwritten Hindi characters from seventy-five writers were considered, along with one printed font, Mangal, to validate the effectiveness of the proposed framework.</p>