We propose a framework for sequence-to-sequence contrastive learning (SeqCLR) of visual
representations, which we apply to text recognition. To account for the sequence-to …

Current text recognition systems, including those for handwritten scripts and scene text, have
relied heavily on image synthesis and augmentation, since it is difficult to realize real-world …

Font generation is a challenging problem especially for some writing systems that consist of
a large number of characters and has attracted a lot of attention in recent years. However …

Automatic font generation remains a challenging research issue due to the large amounts of
characters with complicated structures. Typically, only a few samples can serve as the …

Existing handwritten text generation methods often require more than ten handwriting
samples as style references. However, in practical applications, users tend to prefer a …

Handwriting synthesis, the task of automatically generating realistic images of handwritten
text, has gained increasing attention in recent years, both as a challenge in itself, as well as …

Although current image generation methods have reached impressive quality levels, they
are still unable to produce plausible yet diverse images of handwritten words. On the …

Online and offline handwritten Chinese text recognition (HTCR) has been studied for
decades. Early methods adopted oversegmentation-based strategies but suffered from low …

Many problems can reduce handwritten character recognition performance, such as image
degradation, light conditions, low-resolution images, and even the quality of the capture …

Handwritten Text Recognition has achieved an impressive performance in public
benchmarks. However, due to the high inter-and intra-class variability between handwriting …