Using a synthetic character database for training deep learning models applied to offline handwritten recognition

Publicado en Advances in Intelligent Systems and Computing

 We present our current work on building a deep learning architecture for the offline handwritten character recognition problem. The proposed system is based on training a deep Convolutional Neural Network (CNN) to recognize handwritten characters, using a new synthetic character database derived from UNIPEN dataset. The presented approach is inspired in some successfully-used neural architectures for image classification, specially the VGG-CNN. Our system reads each word with the help of a sliding window in a similar way to how humans do. An innovative feature of our proposal is using a synthetic character database specifically built, in a optimized way, to identify the characters as component elements of the words. Experiments with this new training synthetic dataset produced recognition rates of 98.4% for uppercase and 96.3% for lowercase, respectively.