• Fragmented handwritten digit recognition using grading scheme and fuzzy rules

    • Fulltext

       

        Click here to view fulltext PDF


      Permanent link:
      https://www.ias.ac.in/article/fulltext/sadh/045/0197

    • Keywords

       

      Fragmented handwritten digit; geometrical functions; fuzzy rules; grading scheme; fragmentation threshold.

    • Abstract

       

      The handwritten digit recognition issue turns into one of the well-known issues in machine learning and computer vision applications. Numerous machine learning methods have been utilized to resolve the handwritten digit recognition problem. However, sometimes the digit is not completely present in the image dueto issues related to scanning or environmental conditions (light, illumination, dirt, etc.). Although different efficient methodologies of handwritten digit recognition are proposed, there is not much work done on fragmented handwritten digit recognition. The objective of the proposed research work is to handle this circumstance to assemble a consistent digit recognition system that can precisely handle three types (English, Bangla, and Devanagari) of fragmented handwritten digit images. To solve the confusion, a technique is created to classify handwritten digits based on geometrical functions that are utilized to calculate handwritten digit features to assess if a digit belongs to a specific class. A grading scheme and a set of specified fuzzy rules determine the performance of classification. Experiments have been directed on the three familiar datasets, i.e., MNIST database (English), NumtaDB (Bangla) and Deva numeral database (Devanagari). Since fragmented digit delivers a lesser amount of information, the work also attempts to create a tentative size threshold above which outcomes become erratic and whether such thresholds are standardized or vary depending on other factors. Since the fragmented handwritten digital image does not have a public database, a method is formed to produce repeatable fragmented handwritten digital images from the entire image. Experimental outcomes validate that the proposed approach is effective in recognizing fragmented handwritten digits to an acceptable degree of fragmentation.

    • Author Affiliations

       

      JYOTISMITA CHAKI1 NILANJAN DEY2

      1. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
      2. Deptartment of Information Technology, Techno India College of Technology, Kolkata, India
    • Dates

       
  • Sadhana | News

    • Editorial Note on Continuous Article Publication

      Posted on July 25, 2019

      Click here for Editorial Note on CAP Mode

© 2021-2022 Indian Academy of Sciences, Bengaluru.