Search Results for author:
Found 15 papers, 8 papers with code
DocumentCLIP: Linking Figures and Main Body Text in Reflowed Documents
In this work, we propose DocumentCLIP, a salience-aware contrastive learning framework to enforce vision-language pretraining models to comprehend the interaction between images and longer text within documents.
MGDoc: Pre-training with Multi-granular Hierarchy for Document Image Understanding
In contrast, region-level models attempt to encode regions corresponding to paragraphs or text blocks into a single embedding, but they perform worse with additional word-level features.
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
Towards Language-Free Training for Text-to-Image Generation
One of the major challenges in training text-to-image generation models is the need of a large number of high-quality text-image pairs.
LAFITE: Towards Language-Free Training for Text-to-Image Generation
One of the major challenges in training text-to-image generation models is the need of a large number of high-quality image-text pairs.
Ranked #2 on
Text-to-Image Generation
on Multi-Modal-CelebA-HQ
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.
Deep Visual Template-Free Form Parsing
Automatic, template-free extraction of information from form images is challenging due to the variety of form layouts.
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Despite decades of research, offline handwriting recognition (HWR) of degraded historical documents remains a challenging problem, which if solved could greatly improve the searchability of online cultural heritage archives.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.
Convolutional Neural Networks for Font Classification
This same method also achieves the highest reported accuracy of 86. 6% in predicting paleographic scribal script classes at the page level on medieval Latin manuscripts.
Analysis of Convolutional Neural Networks for Document Image Classification
Convolutional Neural Networks (CNNs) are state-of-the-art models for document image classification tasks.
Ranked #28 on
Document Image Classification
on RVL-CDIP
Document Image Binarization with Fully Convolutional Neural Networks
Binarization of degraded historical manuscript images is an important pre-processing step for many document processing tasks.
