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Automatic text recognition from ancient handwritten record images is animportant problem in the genealogy domain. However, critical challenges such asvarying noise conditions, vanishing texts, and variations in handwriting makethe recognition task difficult. We tackle this problem by developing ahandwritten-to-machine-print conditional Generative Adversarial network(HW2MP-GAN) model that formulates handwritten recognition as atext-Image-to-text-Image translation problem where a given image, typically inan illegible form, is converted into another image, close to its machine-printform. The proposed model consists of three-components including a generator,and word-level and character-level discriminators. The model incorporatesSliced Wasserstein distance (SWD) and U-Net architectures in HW2MP-GAN forbetter quality image-to-image transformation. Our experiments reveal thatHW2MP-GAN outperforms state-of-the-art baseline cGAN models by almost 30 inFrechet 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 tobaseline handwritten recognition models on the IAM database.

Illegible Text to Readable Text: An Image-to-Image Transformation using Conditional Sliced Wasserstein Adversarial Networks