Segmented handwritten text recognition with recurrent neural network classifiers

• Published in: 2015 13th International Conference on Document Analysis and Recognition (ICDAR) pp. 386 - 390
• Main Authors: Bolan Su, Xi Zhang, Shijian Lu, Chew Lim Tan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.08.2015
• Subjects: Artificial neural networks; Computational modeling; Handwriting recognition; Hidden Markov models
• DOI: 10.1109/ICDAR.2015.7333789

Recognition of handwritten text is a useful technique that can be applied in different applications, such as signature recognition, bank check recognition, etc. However, the off-line handwritten text recognition in an unconstrained situation is still a very challenging task due to the high complexity of text strokes and image background. This paper presents a novel segmented handwritten text recognition technique that ensembles recurrent neural network (RNN) classifiers. Two RNN models are first trained that take advantage of the widely used geometrical feature and the Histogram of Oriented Gradient (HOG) feature, respectively. Given a handwritten word image, the optimal recognition result is then obtained by integrating the two trained RNN models together with a lexicon. Experiments on public datasets show the superior performance of our proposed technique.