Abstract

Optical character recognition is gaining immense importance in the domain of deep learning. With each passing day, handwritten digits (0–9) data are increasing rapidly, and plenty of research has been conducted thus far. However, there is still a need to develop a robust model that can fetch useful information and investigate self-build handwritten digit data efficiently and effectively. The convolutional neural network (CNN) models incorporating a sigmoid activation function with a large number of derivatives have low efficiency in terms of feature extraction. Here, we designed a novel CNN model integrated with the extreme learning machine (ELM) algorithm. In this model, the sigmoid activation function is upgraded as the rectified linear unit (ReLU) activation function, and the CNN unit along with the ReLU activation function are used as a feature extractor. The ELM unit works as the image classifier, which makes the perfect symmetry for handwritten digit recognition. A deeplearning4j (DL4J) framework-based CNN-ELM model was developed and trained using the Modified National Institute of Standards and Technology (MNIST) database. Validation of the model was performed through self-build handwritten digits and USPS test datasets. Furthermore, we observed the variation of accuracies by adding various hidden layers in the architecture. Results reveal that the CNN-ELM-DL4J approach outperforms the conventional CNN models in terms of accuracy and computational time.

Highlights

  • Recognizing handwritten digits from their images has been gaining great importance in the21st century

  • Results conclude that the performance of CKELM is higher than Extreme Learning Machine (ELM), RELM, and KELM, especially in terms of accuracy

  • Machine learning algorithms are trendy in the field of image segmentation and image classification because it cannot alter the topological structure of the images

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Summary

Introduction

Recognizing handwritten digits from their images has been gaining great importance in the21st century. Recognizing handwritten digits from their images has been gaining great importance in the. Handwritten digits are used in various online handwritten applications like extracting postal zip codes [1], handling bank cheque amounts [2], and identifying vehicle license-plates [3], etc. All these domains are dealing with datasets and demand high recognition accuracy with smaller computational complexity. The objective of a handwriting digits recognition scheme is to transform handwritten characters images into machine-understandable formats. Handwritten digits [10] are diverse in terms of orientation, size, and distance from the margins, thickness, security systems, and strokes, Symmetry 2020, 12, 1742; doi:10.3390/sym12101742 www.mdpi.com/journal/symmetry

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