An Artificial Neural Network approach for user class-dependent off-line sentence segmentation

In this paper, we present an artificial neural network (ANN) architecture for segmenting unconstrained handwritten sentences in the English language into single words. Feature extraction is performed on a line of text to feed an ANN that classifies each column image as belonging to a word or gap between words. Thus, a sequence of columns of the same class represents words and inter-word gaps. Through experimentation, which was performed using the IAM database, it was determined that the proposed approach achieved better results than the traditional Gap Metric approach for handwriting sentence segmentation.