  Handwritten Text Recognition has achieved an impressive performance in public
benchmarks. However, due to the high inter- and intra-class variability between
handwriting styles, such recognizers need to be trained using huge volumes of
manually labeled training data. To alleviate this labor-consuming problem,
synthetic data produced with TrueType fonts has been often used in the training
loop to gain volume and augment the handwriting style variability. However,
there is a significant style bias between synthetic and real data which hinders
the improvement of recognition performance. To deal with such limitations, we
propose a generative method for handwritten text-line images, which is
conditioned on both visual appearance and textual content. Our method is able
to produce long text-line samples with diverse handwriting styles. Once
properly trained, our method can also be adapted to new target data by only
accessing unlabeled text-line images to mimic handwritten styles and produce
images with any textual content. Extensive experiments have been done on making
use of the generated samples to boost Handwritten Text Recognition performance.
Both qualitative and quantitative results demonstrate that the proposed
approach outperforms the current state of the art.
