Image data augmentation is a machine learning and computer vision technique that artificially expands a training dataset’s size and diversity by applying various transformations to existing images. These transformations can include geometric changes like rotation, scaling, flipping, and cropping, as well as adjustments to color properties such as brightness, contrast, and saturation. By introducing such variations, data augmentation helps models become more robust and generalize better to new, unseen data, effectively reducing the risk of overfitting. This approach is particularly beneficial when the available dataset is limited, as it enhances the model’s ability to handle real-world scenarios with diverse conditions.[1] 

Augmentation and dataset creation of Vehicle Registration Certificates 

We implemented image data augmentation to create and enhance synthetic vehicle registration certificates, aiming to expand and diversify the dataset for improved machine learning model performance and robustness. This technique enables the generation of new image variations, enhancing the model’s ability to recognize and analyze visual information. augmentation methods such as rotation, contrast adjustment, cropping, and text addition are employed. These manipulations introduce variability in image appearance while preserving data accuracy. This approach ensures the development of models that can accurately extract and identify relevant information, even from images with varying visual characteristics, scanning conditions, or those captured using a smartphone instead of a scanner.