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Handwritten digit recognition based on corner detection and convolutional neural network
| Content Provider | Scilit |
|---|---|
| Author | Zhong, Chen Wang, Yuhang Zhang, Decheng Wang, Ke |
| Copyright Year | 2020 |
| Description | Journal: Journal of Physics: Conference Series In the field of inspection and testing, it is necessary to manually fill a large number of test record forms, and the digital information accounts for a large proportion in the record forms. For the purpose of automatic and accurate recognition of handwritten form data, this paper proposes a handwritten digit recognition method based on Harris corner detection algorithm and convolutional neural network(CNN). The Harris corner detection algorithm is used to identify the positioning marks in the form image, which determine the possible fill-in positions of handwritten digits. Through a 14-layer CNN model, and using the ReLu function as the excitation function, the handwritten digit features of the target area in the image are recognized. The experimental results show that the average recognition accuracy rate of this method on the test set can reach 98.14%. So a technical solution for intelligent recognition of record forms is thus provided. |
| Related Links | https://iopscience.iop.org/article/10.1088/1742-6596/1651/1/012165/pdf |
| ISSN | 17426588 |
| e-ISSN | 17426596 |
| DOI | 10.1088/1742-6596/1651/1/012165 |
| Journal | Journal of Physics: Conference Series |
| Issue Number | 1 |
| Volume Number | 1651 |
| Language | English |
| Publisher | IOP Publishing |
| Publisher Date | 2020-11-01 |
| Access Restriction | Open |
| Subject Keyword | Journal: Journal of Physics: Conference Series Handwritten Digit |
| Content Type | Text |
| Resource Type | Article |
| Subject | Physics and Astronomy |
