Improving Handwritten OCR with Training Samples Generated by Glyph
  Conditional Denoising Diffusion Probabilistic Model
Haisong Ding Bozhi Luan Dongnan Gui Kai Chen Qiang Huo
Constructing a highly accurate handwritten OCR system requires large amounts
of representative training data, which is both time-consuming and expensive to
collect. To mitigate the issue, we propose a denoising diffusion probabilistic
model (DDPM) to generate training samples. This model conditions on a printed
glyph image and creates mappings between printed characters and handwritten
images, thus enabling the generation of photo-realistic handwritten samples
with diverse styles and unseen text contents. However, the text contents in
synthetic images are not always consistent with the glyph conditional images,
leading to unreliable labels of synthetic samples. To address this issue, we
further propose a progressive data filtering strategy to add those samples with
a high confidence of correctness to the training set. Experimental results on
IAM benchmark task show that OCR model trained with augmented DDPM-synthesized
training samples can achieve about 45% relative word error rate reduction
compared with the one trained on real data only.