Structured text understanding on Visually Rich Documents (VRDs) is a crucial part of
Document Intelligence. Due to the complexity of content and layout in VRDs, structured text …

State-of-the-art solutions for Natural Language Processing (NLP) are able to capture a
broad range of contexts, like the sentence-level context or document-level context for short …

Classifying heterogeneous visually rich documents is a challenging task. Difficulty of this
task increases even more if the maximum allowed inference turnaround time is constrained …

Abstractive summarization at controllable lengths is a challenging task in natural language
processing. It is even more challenging for domains where limited training data is available …

Information Extraction (IE) from semi-structured web-pages is a long studied problem.
Training a model for this extraction task requires a large number of human-labeled samples …

Extracting structured information from templatic documents is an important problem with the
potential to automate many real-world business workflows such as payment, procurement …

Along with textual content, visual features play an essential role in the semantics of visually
rich documents. Information extraction (IE) tasks perform poorly on these documents if these …

Building automatic extraction models for visually rich documents like invoices, receipts, bills,
tax forms, etc. has received significant attention lately. A key bottleneck in developing …

Visually rich documents (VRD) are physical/digital documents that utilize visual cues to
augment their semantics. The information contained in these documents are often …

A visually rich document (VRD) utilizes visual features along with linguistic cues to
disseminate information. Training a custom extractor that identifies named entities from a …