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Found 8 papers, 7 papers with code
Leveraging Vision-Language Foundation Models for Fine-Grained Downstream Tasks
Vision-language foundation models such as CLIP have shown impressive zero-shot performance on many tasks and datasets, especially thanks to their free-text inputs.
Faster DAN: Multi-target Queries with Document Positional Encoding for End-to-end Handwritten Document Recognition
Recent advances in handwritten text recognition enabled to recognize whole documents in an end-to-end way: the Document Attention Network (DAN) recognizes the characters one after the other through an attention-based prediction process until reaching the end of the document.
Towards End-to-end Handwritten Document Recognition
We proposed an approach at the line level, based on a fully convolutional network, in order to design a first generic feature extraction step for the handwriting recognition task.
DAN: a Segmentation-free Document Attention Network for Handwritten Document Recognition
For the first time, we propose an end-to-end segmentation-free architecture for the task of handwritten document recognition: the Document Attention Network.
Ranked #1 on
Handwritten Text Recognition
on READ 2016
SPAN: a Simple Predict & Align Network for Handwritten Paragraph Recognition
Unconstrained handwriting recognition is an essential task in document analysis.
Ranked #4 on
Handwritten Text Recognition
on READ2016(line-level)
Recurrence-free unconstrained handwritten text recognition using gated fully convolutional network
Unconstrained handwritten text recognition is a major step in most document analysis tasks.
Ranked #5 on
Handwritten Text Recognition
on IAM(line-level)
Have convolutions already made recurrence obsolete for unconstrained handwritten text recognition ?
This has a direct influence on the training time of such architectures, with also a direct consequence on the time required to explore various architectures.
End-to-end Handwritten Paragraph Text Recognition Using a Vertical Attention Network
For each text line features, a decoder module recognizes the character sequence associated, leading to the recognition of a whole paragraph.
Ranked #2 on
Handwritten Text Recognition
on READ2016(line-level)
