Search Results for author:
Found 9 papers, 6 papers with code
Certified Neural Network Watermarks with Randomized Smoothing
Watermarking is a commonly used strategy to protect creators' rights to digital images, videos and audio.
End-to-end Document Recognition and Understanding with Dessurt
Dessurt is a more flexible model than prior methods and is able to handle a variety of document domains and tasks.
Ranked #31 on
Visual Question Answering (VQA)
on DocVQA test
Was my Model Stolen? Feature Sharing for Robust and Transferable Watermarks
The main idea is to insert a watermark which is only known to defender into the protected model and the watermark will then be transferred into all stolen models.
RPCL: A Framework for Improving Cross-Domain Detection with Auxiliary Tasks
Contrasted with prior work, this paper provides a complementary solution to align domains by learning the same auxiliary tasks in both domains simultaneously.
Text and Style Conditioned GAN for Generation of Offline Handwriting Lines
This paper presents a GAN for generating images of handwritten lines conditioned on arbitrary text and latent style vectors.
Cross-Domain Document Object Detection: Benchmark Suite and Method
We establish a benchmark suite consisting of different types of PDF document datasets that can be utilized for cross-domain DOD model training and evaluation.
Start, Follow, Read: End-to-End Full-Page Handwriting Recognition
Despite decades of research, offline handwriting recognition (HWR) of degraded historical documents remains a challenging problem, which if solved could greatly improve the searchability of online cultural heritage archives.
Ranked #12 on
Handwritten Text Recognition
on IAM
Language Model Supervision for Handwriting Recognition Model Adaptation
Training state-of-the-art offline handwriting recognition (HWR) models requires large labeled datasets, but unfortunately such datasets are not available in all languages and domains due to the high cost of manual labeling. We address this problem by showing how high resource languages can be leveraged to help train models for low resource languages. We propose a transfer learning methodology where we adapt HWR models trained on a source language to a target language that uses the same writing script. This methodology only requires labeled data in the source language, unlabeled data in the target language, and a language model of the target language.
PageNet: Page Boundary Extraction in Historical Handwritten Documents
When digitizing a document into an image, it is common to include a surrounding border region to visually indicate that the entire document is present in the image.
