A Novel Technique for Handwritten Digit Recognition Using Deep Learning

Handwritten digit recognition (HDR) shows a significant application in the area of information processing. However, correct such characters from images is complicated task due to immense variations writing style people. Moreover, occurrence several image artifacts like existence intensity variations, blurring, and noise complicates this process. In proposed method, we have tried overcome aforementioned limitations by introducing deep learning- (DL-) based technique, namely, EfficientDet-D4, for numeral categorization. Initially, input are annotated exactly show region interest (ROI). next phase, these used train EfficientNet-B4-based EfficientDet-D4 model detect categorize numerals into their respective classes zero nine. We tested over MNIST dataset demonstrate its efficacy attained an average accuracy value 99.83%. Furthermore, accomplished cross-dataset evaluation on USPS database achieved 99.10%. Both visual reported experimental results that our method can accurately classify HDR even with varying under presence various sample noise, chrominance, position, size numerals. introduced approach capable generalizing well unseen cases which confirms effective solution recognition.