A Novel Technique for Handwritten Digit Recognition Using Deep Learning

Abstract

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

Advancing Multilingual Handwritten Numeral Recognition With Attention-Driven Transfer Learning

“…Study [30] presents an EfficientDet-D4 model for recognizing handwritten digits. EfficientDet-D4 is based on EfficientNet-B4, which is a highly efficient convolutional neural network that balances depth, width, and resolution using compound scaling and advanced techniques, achieving state-of-the-art accuracy on various computer vision tasks.…”

Research on Asphalt Pavement Disease Detection Based on Improved YOLOv5s

“…With the continuous development of computer technology, object detection algorithm based on deep learning have achieved rapid development and has been widely used in autonomous driving, face recognition, crop disease and pest recognition, defect detection, and other fields [5][6][7]. There are two main types of object detection algorithms based on deep learning: One is a two-stage target detection algorithm that divides feature extraction and target localization into two stages, such as R-CNN [8,9] (Region Proposals for Convolutional Neural Networks), Fast R-CNN [10], and Faster R-CNN [11][12][13]. The second category is a one-stage target detection algorithm that integrates feature extraction and location processing, such as SSD [14] (Single Shot Multibox Detector) and YOLO [15,16] (You Only Look Once) series.…”

Integrated Mediapipe with a CNN Model for Arabic Sign Language Recognition

“…Communication using hand gestures is the most signifcant component of sign language since they are employed in every element of human communication. For example, they can be used to accompany speech or to communicate on their own in a setting with a lot of background noise because signers communicate the majority of their information with their hands [4]. So, in order to incorporate all members of the community, regardless of their abilities, we intend to construct a robust automatic sign language translation and recognition system to address these issues.…”

“…Tis may be accomplished through the utilization of techniques such as CNNs, the histogram of oriented gradients (HOG), and local binary patterns (LBPs). Finally, the obtained data are used by machine-learning methods including support vector machines (SVMs), decision trees (DTs), and artifcial neural networks (ANNs) to classify the sign language gesture [4].…”