Int J Performability Eng ›› 2023, Vol. 19 ›› Issue (4): 273-282.doi: 10.23940/ijpe.23.04.p6.273282

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Descriptive Handwritten Paper Grading System using NLP and Fuzzy Logic

Bhushan Nandwalkar*, Sukruta Pardeshi, Makarand Shahade, and Ashish Awate   

  1. Department of Computer Engineering, SVKM’s Institute of Technology, Dhule, India
  • Contact: *E-mail address:

Abstract: The rapid changes in the educational sector driven by the daily growth in technology breakthroughs have produced a very effective learning environment. Assessment is crucial to ascertain how well students learn and the amount of relevant knowledge and skills they have mastered. Current systems have limitations concerning volume, manpower, and variety in assessment methodologies. A physical paper evaluation is very repetitive, difficult, and complex and entails numerous logistical operations. Such a handwritten paper grading technique steadily increases the length of time needed to examine the answers and does not guarantee correctness in scoring the answers. Online evaluation cannot guarantee the correctness of the solutions supplied by other test takers. The solution is to make the examiner’s job easier while reviewing papers and judging how creatively pupils responded to the questions. This inspired the development of an online automatic grading system that grades students' handwritten papers. Natural Language Processing methods like TF-IDF and BERT can be used to determine the count of important keyword frequencies in students' responses, and how closely the text matches the original answer. An inference system that uses fuzzy logic can later be used to grade the responses. Therefore, this paper proposes an online grading system that combines NLP and Fuzzy Logic to score and evaluate fellow students’ handwritten papers.

Key words: NLP, Fuzzy Logic, TF-IDF, BERT, Handwritten OCR Recognition