Writers identification\verification using handwriting biometrics are used in forensic analysis, document verification, and security systems, they have attracted a lot of attention as a means of identifying and verifying writers. Both Arabic and English texts still present difficulties especially in Arabic because of the script's cursive style and the minute variations in letter placement, which are frequently indicated by the positioning of dots. This research presents a novel offline system for identification/verification Arabic and English handwriting that uses convolutional neural networks (CNNs) in conjunction with several techniques, such as Harris Corner Detector, Shi-Tomasi, and SIFT. The technique does not require word or character segmentation and employs data augmentation to enhance the quality of the training data and upsampling to enhance image clarity. Results from experiments show that the model is more effective than previous methods used in the literature.