ScrabbleGAN: Semi-Supervised Varying Length Handwritten Text Generation
Table 3: Domain adaptation results from the IAM dataset to the CVL dataset. First row is naive approach of using a net trained on IAM. Next three rows show the effect of 100k synthetic images having either CVL style, CVL lexicon or both. The bottom row shows the oracle performance of supervised training on the CVL train set, just for reference. No CVL labels were used to train HTR, except for the oracle.
['Train data', 'Style', 'Lex.', 'WER[%]', 'NED[%]']
For the case at hand, we look at methods that use HTG or similar approaches to learn augmentation of the handwritten examples. One straightforward example of this is a method proposed by Bhunia et al. Krishanan el al. First, we wish to emphasize the 17% WER gap between the naive approach and the oracle, showing how hard it is for the selected HTR to generalize in this case. Second, we observe that synthesizing images with CVL style and IAM lexicon (second row) does not alter the results compared to the naive approach. On the other hand, synthesizing images with IAM style and CVL lexicon (third row) boosts WER performance by about 5%. Finally, synthesizing images with both CVL style and lexicon (fourth row) yields another 5% boost in WER, with NED score that is better than the oracle.