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Found 9 papers, 3 papers with code
SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion
We introduce SmolDocling, an ultra-compact vision-language model targeting end-to-end document conversion.
Docling: An Efficient Open-Source Toolkit for AI-driven Document Conversion
We introduce Docling, an easy-to-use, self-contained, MIT-licensed, open-source toolkit for document conversion, that can parse several types of popular document formats into a unified, richly structured representation.
Docling Technical Report
This technical report introduces Docling, an easy to use, self-contained, MIT-licensed open-source package for PDF document conversion.
KVP10k : A Comprehensive Dataset for Key-Value Pair Extraction in Business Documents
In recent years, the challenge of extracting information from business documents has emerged as a critical task, finding applications across numerous domains.
ICDAR 2023 Competition on Robust Layout Segmentation in Corporate Documents
The results demonstrate substantial progress towards achieving robust and highly generalizing methods for document layout understanding.
Optimized Table Tokenization for Table Structure Recognition
The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average.
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.
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.
