Digitization of Handwritten Attendance Entries using Structural Similarity Measurement

The aim of this research is to automatize handwritten attendance data entries in digital form with a minimal human intervention using Handwritten Character Recognition (HCR). Here, all the preprocessing techniques (Image Registration, Cropping, Erosion. Dilation, Noise removal, Line removal, Segmentation), and classification technique are implemented in the MATLAB platform. Various types of techniques for recognition of handwritten digits and characters have been implemented for different applications. Handwritten entries in the attendance sheet are classified using Structural Similarity Measurement (SSIM). The proposed system has achieved a 95.60% classification accuracy.