Increasing the Accuracy of Writer Identification Based on Bee Colony Optimization Algorithm and Hybrid Deep Learning Method

It is one of the most important and challenging classification issues to identify the writer's identity from offline handwriting images, which has been the focus of many researchers in recent years. This article presents a novel approach to identifying the author of offline Pertian manuscripts from scanned images based on deep convolutional neural networks. For the first time in the proposed network, the bee colony algorithm has been used in the middle layers of a deep convolutional neural network in order to improve the accuracy of identifying the author and to optimize the parameters, as well as improve the learning performance. In terms of the presented scenario, it was tested independently of the written language in both Persian and English. The proposed method is more accurate than previous studies for the IMA dataset, with an accuracy of 97.60%. Moreover, for the Firemaker dataset, the proposed model has significantly improved over the existing results, with the accuracy of the current model being 99.71%, a value that is 1.78% higher than the results of the previous models.

@article{Libo2024,
  title     = {Increasing the Accuracy of Writer Identification Based on Bee Colony Optimization Algorithm and Hybrid Deep Learning Method},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {6},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Hao Libo and Xu Jingqi},
  doi       = {10.14569/IJACSA.2024.0150631},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150631}
}