Research & development

Deep Learning Starts With Ourselves

Introduction

Applica is always improving: Our team works hard to develop new innovations in document understanding and add additional features.

Researched, Reviewed, Published

Applica’s R&D team regularly publishes research papers about the breakthroughs we’ve achieved.
Going Full-TILT Boogie on Document Understanding with Text-Image-Layout Transformer
Authors
Rafał Powalski, Łukasz Borchmann, Dawid Jurkiewicz, Tomasz Dwojak, Michał Pietruszka
Date
2021
We address the challenging problem of Natural Language Comprehension beyond plain-text documents by introducing the TILT neural network architecture which simultaneously learns layout information, visual features, and textual semantics. Contrary to previous approaches, we rely on a decoder capable of unifying a variety of problems involving natural language. The layout is represented as an attention bias and complemented with contextualized visual information, while the core of our model is a pretrained encoder-decoder Transformer. Our novel approach achieves state-of-the-art results in extracting information from documents and answering questions which demand layout understanding (DocVQA, CORD, SROIE). At the same time, we simplify the process by employing an end-to-end model.
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Kleister: Key Information Extraction Datasets Involving Long Documents with Complex Layouts
Authors
Tomasz Stanisławek, Filip Graliński, Anna Wróblewska, Dawid Lipiński, Agnieszka Kaliska, Paulina Rosalska, Bartosz Topolski, Przemysław Biecek
Date
2021
The relevance of the Key Information Extraction (KIE) task is increasingly important in natural language processing problems. But there are still only a few well-defined problems that serve as benchmarks for solutions in this area. To bridge this gap, we introduce two new datasets (Kleister NDA and Kleister Charity). They involve a mix of scanned and born-digital long formal English-language documents. In these datasets, an NLP system is expected to find or infer various types of entities by employing both textual and structural layout features. The Kleister Charity dataset consists of 2,788 annual financial reports of charity organizations, with 61,643 unique pages and 21,612 entities to extract. The Kleister NDA dataset has 540 Non-disclosure Agreements, with 3,229 unique pages and 2,160 entities to extract. We provide several state-of-the-art baseline systems from the KIE domain (Flair, BERT, RoBERTa, LayoutLM, LAMBERT), which show that our datasets pose a strong challenge to existing models. The best model achieved an 81.77{\%} and an 83.57{\%} F1-score on respectively the Kleister NDA and the Kleister Charity datasets. We share the datasets to encourage progress on more in-depth and complex information extraction tasks.
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Awards

Our Solution Has Won Multiple Prizes

Applica’s solution regularly wins awards and competitions around the world.
April 2021
Applica’s innovative TILT model crushed the competition in the ICDAR Infographics VQA Challenge
March 2021
Applica continues to dominate the venerated Key Information Extraction Competition
February 2021
Applica beats all other AI solutions in the Document Visual Question Answering Challenge
February 2021
The Applica team wins Best Paper at SemEval 2020

Meet the Technology

Find out what makes Applica’s approach to document automation so special—and so much more powerful than other approaches.
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Dive Into the Details

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