  Automatic text recognition from ancient handwritten record images is an
important problem in the genealogy domain. However, critical challenges such as
varying noise conditions, vanishing texts, and variations in handwriting make
the recognition task difficult. We tackle this problem by developing a
handwritten-to-machine-print conditional Generative Adversarial network
(HW2MP-GAN) model that formulates handwritten recognition as a
text-Image-to-text-Image translation problem where a given image, typically in
an illegible form, is converted into another image, close to its machine-print
form. The proposed model consists of three-components including a generator,
and word-level and character-level discriminators. The model incorporates
Sliced Wasserstein distance (SWD) and U-Net architectures in HW2MP-GAN for
better quality image-to-image transformation. Our experiments reveal that
HW2MP-GAN outperforms state-of-the-art baseline cGAN models by almost 30 in
Frechet Handwritten Distance (FHD), 0.6 on average Levenshtein distance and 39%
in word accuracy for image-to-image translation on IAM database. Further,
HW2MP-GAN improves handwritten recognition word accuracy by 1.3% compared to
baseline handwritten recognition models on the IAM database.
