Version 1
: Received: 25 December 2023 / Approved: 26 December 2023 / Online: 27 December 2023 (01:47:57 CET)
Version 2
: Received: 27 December 2023 / Approved: 27 December 2023 / Online: 27 December 2023 (10:25:13 CET)
How to cite:
Sambath, S. Novel Approach to High Accuracy and Efficiency Optical Character Recognizer for Handwritten Digits. Preprints2023, 2023121964. https://doi.org/10.20944/preprints202312.1964.v1
Sambath, S. Novel Approach to High Accuracy and Efficiency Optical Character Recognizer for Handwritten Digits. Preprints 2023, 2023121964. https://doi.org/10.20944/preprints202312.1964.v1
Sambath, S. Novel Approach to High Accuracy and Efficiency Optical Character Recognizer for Handwritten Digits. Preprints2023, 2023121964. https://doi.org/10.20944/preprints202312.1964.v1
APA Style
Sambath, S. (2023). Novel Approach to High Accuracy and Efficiency Optical Character Recognizer for Handwritten Digits. Preprints. https://doi.org/10.20944/preprints202312.1964.v1
Chicago/Turabian Style
Sambath, S. 2023 "Novel Approach to High Accuracy and Efficiency Optical Character Recognizer for Handwritten Digits" Preprints. https://doi.org/10.20944/preprints202312.1964.v1
Abstract
The automated transcription of handwritten characters into a legible output is a multi-faceted process with diverse applications. In this paper, a novel approach to optical character recognition (OCR) for handwritten digits is proposed that, in certain components, exceeds current architectures in terms of accuracy, effectiveness, adjustability, temporal efficiency, and/or computational simplicity. This model succeeds in the adoption and enhancement of deprecated or obsolete algorithms across eight steps of image pre-processing—normalization, grayscaling, thresholding/binarization, noise removal, skew-correction, skeletonization/thinning, line separation, and character segmentation. The aforementioned model is evaluated with the use of a Convolutional Neural Network (CNN) leveraging the Balanced eMNIST dataset for training and testing. By suggesting contour-based feature extraction methods as alternatives to pixel-by-pixel iteration, the proposed paradigm demonstrates its capacity to serve as a suitable alternative to current, commonly used algorithms and computational techniques for textual image classification.
Keywords
Optical Character Recognition; Handwritten Digits; Convolutional Neural Network; Textual Image Classification; Skew Correction; Skeletonization/Thinning; Character Mapping
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.