Active Transfer Learning for Handwriting Recognition

Eric Burdett, Stanley Fujimoto, Timothy Brown, Ammon Shurtz, Daniel Segrera, Lawry Sorenson, Mark J. Clement, Joseph Price

Abstract: With the advent of deep neural networks, handwriting recognition systems have recently achieved remarkable performance. Unfortunately, to achieve high-quality results, these models require large amounts of labeled training data, which is difficult to obtain. Various methods have been proposed to reduce the volume of training data required. We propose a framework for fitting new handwriting recognition models that joins both active and transfer learning into a unified framework. Empirical results show that our method performs better than traditional active learning, transfer learning, and standard supervised training methods.