Handwritten digit segmentation: Is it still necessary?

Over the last decades, a great deal of research has been devoted to handwritten digit segmentation. Algorithms based on different features extracted from the background, foreground, and contour of images have been proposed, with those achieving the best results usually relying on a heavy set of heuristics and over-segmentation. Here, the challenge lies in finding a good set of heuristics to reduce the number of segmentation hypotheses. Independently of the heuristic over-segmentation strategy adopted, all algorithms used show their limitations when faced with complex cases such as overlapping digits. In this work, we postulate that handwritten digit segmentation can be successfully replaced by a set of classifiers trained to predict the size of the string and classify them without any segmentation. To support our position, we trained four Convolutional Neural Networks (CNN) on data generated synthetically and validated the proposed method on two well-known databases, namely, the Touching Pairs Dataset and NIST SD19. Our experimental results show that the CNN classifiers can handle complex cases of touching digits more efficiently than all segmentation algorithms available in the literature.