Data augmentation is a technique used in handwriting recognition to enhance the diversity and volume of training datasets without the need for collecting new data. It involves creating modified versions of existing handwritten samples to improve the robustness of machine learning models. This can help the models generalize better to variations they might encounter in real-world applications, such as different handwriting styles or writing conditions.
One common method of data augmentation for handwriting recognition is geometric transformation. This includes operations such as rotation, scaling, and translation. For example, a sample of handwritten text can be slightly rotated or shifted horizontally or vertically. This simulates the variability that occurs when different individuals write, making the model less sensitive to the exact positioning of text. Additionally, varying the size of the text can help the model learn to recognize characters that may appear larger or smaller depending on the writer's hand.
Another approach involves applying image-based techniques, such as adding noise or changing brightness and contrast. For instance, a handwritten character image can have random noise added to it, mimicking the imperfections that might occur on a physical page, like smudges or shadows. Furthermore, random distortions can be applied to simulate different writing instruments, which affects the stroke thickness and style. By implementing these augmentation techniques, developers can vastly increase the dataset’s size and variability, leading to improved accuracy and robustness in handwriting recognition systems.