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.v2
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.v2
Sambath, S. Novel Approach to High Accuracy and Efficiency Optical Character Recognizer for Handwritten Digits. Preprints2023, 2023121964. https://doi.org/10.20944/preprints202312.1964.v2
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.v2
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.v2
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.
Commenter: Sanjith Sambath
Commenter's Conflict of Interests: Author