Search Results for author:
Found 7 papers, 3 papers with code
Unsupervised Term Extraction for Highly Technical Domains
Developing term extractors that are able to generalize across very diverse and potentially highly technical domains is challenging, as annotations for domains requiring in-depth expertise are scarce and expensive to obtain.
BusiNet -- a Light and Fast Text Detection Network for Business Documents
Business documents often include sensitive information and as such they cannot be uploaded to a cloud service for OCR.
TableFormer: Table Structure Understanding with Transformers
In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders.
pNLP-Mixer: an Efficient all-MLP Architecture for Language
On MTOP our pNLP-Mixer almost matches the performance of mBERT, which has 38 times more parameters, and outperforms the state-of-the-art of tiny models (pQRNN) with 3 times fewer parameters.
Unsupervised Domain Generalization by Learning a Bridge Across Domains
The ability to generalize learned representations across significantly different visual domains, such as between real photos, clipart, paintings, and sketches, is a fundamental capacity of the human visual system.
Robust PDF Document Conversion Using Recurrent Neural Networks
In this paper, we present a novel approach to document structure recovery in PDF using recurrent neural networks to process the low-level PDF data representation directly, instead of relying on a visual re-interpretation of the rendered PDF page, as has been proposed in previous literature.
An Information Extraction and Knowledge Graph Platform for Accelerating Biochemical Discoveries
Information extraction and data mining in biochemical literature is a daunting task that demands resource-intensive computation and appropriate means to scale knowledge ingestion.
