Hybrid machine learning techniques for gender identification from handwritten images using textural features

Gender identification and classification from handwritten images are a challenging problem that has attracted considerable attention recently. Automatically determining a person's gender from their handwriting is the mission. In this research, we employ support vector machine (SVM) and principal component analysis (PCA) to develop a novel approach to gender categorization based on handwritten picture data. The proposed method begins by applying PCA to reduce the dimensionality of the input data. This step is important because handwritten images can be high-dimensional, which can lead to overfitting and poor generalization. After reducing the dimensionality, the PCA-transformed data are fed into an SVM classifier for gender identification and classification. We evaluate the performance of our method on a publicly available dataset of handwritten images, and our experimental results show that our method achieves high accuracy in gender identification, outperforming other classification methods. While our results are promising, several challenges still need to be addressed. For example, the quality of the input images can significantly impact the accuracy of gender identification. In addition, the model may be biased towards certain styles of handwriting or writing instruments. Future work in this area could focus on improving the quality of the input images or exploring ways to mitigate bias in the model. Our proposed method for gender identification and classification from handwritten images using SVM with PCA is a promising approach with potential applications in the fields such as forensic analysis, document processing, and automated handwriting recognition. Further research in this area could lead to more accurate and effective methods for gender identification and classification from handwritten data.