Search Results for author:
Found 17 papers, 4 papers with code
Privacy-Aware Document Visual Question Answering
We employ a federated learning scheme, that reflects the real-life distribution of documents in different businesses, and we explore the use case where the ID of the invoice issuer is the sensitive information to be protected.
Document Understanding Dataset and Evaluation (DUDE)
We call on the Document AI (DocAI) community to reevaluate current methodologies and embrace the challenge of creating more practically-oriented benchmarks.
Hierarchical multimodal transformers for Multi-Page DocVQA
The proposed method is based on a hierarchical transformer architecture where the encoder summarizes the most relevant information of every page and then, the decoder takes this summarized information to generate the final answer.
OCR-IDL: OCR Annotations for Industry Document Library Dataset
It is our hope that OCR-IDL can be a starting point for future works on Document Intelligence.
ICDAR 2021 Competition on Document VisualQuestion Answering
In this report we present results of the ICDAR 2021 edition of the Document Visual Question Challenges.
EKTVQA: Generalized use of External Knowledge to empower Scene Text in Text-VQA
The open-ended question answering task of Text-VQA often requires reading and reasoning about rarely seen or completely unseen scene-text content of an image.
Open-Ended Question Answering
Optical Character Recognition (OCR)
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Document Collection Visual Question Answering
Current tasks and methods in Document Understanding aims to process documents as single elements.
InfographicVQA
Infographics are documents designed to effectively communicate information using a combination of textual, graphical and visual elements.
Multimodal grid features and cell pointers for Scene Text Visual Question Answering
This paper presents a new model for the task of scene text visual question answering, in which questions about a given image can only be answered by reading and understanding scene text that is present in it.
ICDAR 2019 Competition on Scene Text Visual Question Answering
ST-VQA introduces an important aspect that is not addressed by any Visual Question Answering system up to date, namely the incorporation of scene text to answer questions asked about an image.
Scene Text Visual Question Answering
Current visual question answering datasets do not consider the rich semantic information conveyed by text within an image.
Beyond Visual Semantics: Exploring the Role of Scene Text in Image Understanding
Images with visual and scene text content are ubiquitous in everyday life.
Don't only Feel Read: Using Scene text to understand advertisements
We propose a framework for automated classification of Advertisement Images, using not just Visual features but also Textual cues extracted from embedded text.
Learning Cross-Modal Deep Embeddings for Multi-Object Image Retrieval using Text and Sketch
In this work we introduce a cross modal image retrieval system that allows both text and sketch as input modalities for the query.
R-PHOC: Segmentation-Free Word Spotting using CNN
This paper proposes a region based convolutional neural network for segmentation-free word spotting.
Visual attention models for scene text recognition
A set of feature vectors are derived from an intermediate convolutional layer corresponding to different areas of the image.
Query by String word spotting based on character bi-gram indexing
Both the documents and query strings are encoded using a recently proposed word representa- tion that projects images and strings into a common atribute space based on a pyramidal histogram of characters(PHOC).
