Abstract
Persian handwritten character recognition (PHCR) is one of the challenging issues in machine vision. This study aims to investigate the performance of convolutional neural networks (CNN) on PHCR problems. To investigate the performance of CNN methods, a dataset of Persian handwritten characters has been used as ground truth data. The dataset elements converted into images with the size of 64 × 64 picture elements (pixels). To clarify the outperformance of proposed method, it is compared with notable conventional techniques in PHCR problems. The results show that the performance of conventional methods is not impressive on PHCR. In addition to conventional methods, two types of CNN methods have been implemented. Single convolutional neural network (SCNN) has been implemented based on the simple structure of CNN (LeNet-5). Moreover, the bagging paradigm applied on CNN to extend it into ensemble convolutional neural network (ECNN) with a variety of network parameters. The results show that, although the ECNN outperform SCNN (accuracy=97.1%) in accuracy, SCNN can recognize Persian handwritten characters with bearable complexity and fair accuracy (accuracy=96.3%).
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
