A Convolution Neural Network, Particle Swarm Optimization Hybrid Model for Scripting Language Handwritten Character Recognition
DOI:
https://doi.org/10.26438/ijcse/v13i8.3041Keywords:
Data analysis, machine learning, deep learningAbstract
In the realm of character recognition, the availability of comprehensive and relevant datasets is essential for developing accurate and robust models. In this research, we address the dearth of datasets for Pashto handwritten character recognition by introducing a novel and extensive dataset, previously unavailable in the field. The absence of a Pashto dataset posed a significant challenge to developing effective recognition models, making this dataset a valuable contribution to the research community. To optimize the recognition process, we put forward a CNN-PSO (Convolutional Neural Network–Particle Swarm Optimization) based hybrid approach. The CNN component is utilized for extracting features, while PSO is applied for parameter optimization. By incorporating PSO, the aim is to strengthen the model’s capacity to accurately recognize and classify handwritten Pashto characters. To validate this approach, we compare the CNN-PSO framework with a standard CNN baseline. The outcomes clearly indicate that the hybrid design surpasses the performance of the CNN-only model in Pashto handwritten character recognition. The proposed research findings reveal the potential of hybrid models in character recognition tasks and underline the significance of utilizing the Pashto dataset in advancing research in this domain. This study aids in building recognition systems for Pashto that are more precise and effective, with possible applications in OCR technologies and language processing for Pashto-speaking regions.
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