Handwritten character recognition using convolutional neural network
Handwritten character recognition (HCR) is the detection of characters from images, documents and other sources and changes them in machine-readable shape for further processing. The accurate recognition of intricate-shaped compound handwritten characters is still a great challenge. Recent advances...
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| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IOP Publishing
2021
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/31546/1/Handwritting%20character%20recognition.pdf http://umpir.ump.edu.my/id/eprint/31546/ https://iopscience.iop.org/journal/1742-6596 |
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| Institution: | Universiti Malaysia Pahang |
| Language: | English |
| Summary: | Handwritten character recognition (HCR) is the detection of characters from images, documents and other sources and changes them in machine-readable shape for further processing. The accurate recognition of intricate-shaped compound handwritten characters is still a great challenge. Recent advances in convolutional neural network (CNN) have made great progress in HCR by learning discriminatory characteristics from large amounts of raw data. In this paper, CNN is implemented to recognize the characters from a test dataset. The main focus of this work is to investigate CNN capability to recognize the characters from the image dataset and the accuracy of recognition w implementation is experimented with the dataset NIST to obtain the accuracy of handwritten characters. Test result provides that an accuracy of 92.91% accuracy is obtained on 200 images with a training set of 1000 images from NIST. |
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