Optical Recognition of Handwritten by Aiding Computer Vision and Deep Learning

Automatic handwriting detection is becoming a serious difficulty in image processing. In addition, this may be considered a crucial function in the era of technology 4.0. This study describes a technique for detecting handwriting using computer vision and deep learning. During image processing, the original picture is offered to undergo noise reduction and filtering. The input photos will then be divided into two calibrated groups: RGB images and binary images for the model of the enhanced ALEXNET Neuron Network. In addition, a graphical user interface for combining several NN models is created. The test results reveal that the proposed technique is 96.95% effective under varied lighting conditions. In addition, the training model is compared to other NN models to evaluate the effectiveness of the suggested method.