Research and Application of Digit Recognition Based on K-Nearest Neighbor Classifier
- DOI
- 10.2991/978-94-6463-370-2_5How to use a DOI?
- Keywords
- optical recognition; number; image
- Abstract
The electrical or mechanical translation of pictures of typed, handwritten, or printed text into machine-encoded text is known as optical character recognition (OCR). Pattern recognition, artificial intelligence, and computer vision are all areas of study in OCR. Early iterations operated on one typeface at a time and required training with photos of each character. modern systems that bring high precision to many fonts have now become a natural choice, supporting various image file formats for input. However, little research has been done on programs that specifically recognize numbers. The goal of this article is to perform digit recognition based on the KNN (k-nearest neighbor classifier). For digit recognition based on the KNN classifier, one can use the KNN algorithm to classify each character in an image. The image is first preprocessed and then each digit is segmented. The features of each digit are then extracted and used as input to the KNN classifier. The dataset comes from the MNIST (Modified National Institute of Standards and Technology database) dataset.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Bolin Lu PY - 2024 DA - 2024/02/14 TI - Research and Application of Digit Recognition Based on K-Nearest Neighbor Classifier BT - Proceedings of the 2023 International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2023) PB - Atlantis Press SP - 36 EP - 42 SN - 1951-6851 UR - https://doi.org/10.2991/978-94-6463-370-2_5 DO - 10.2991/978-94-6463-370-2_5 ID - Lu2024 ER -