Please break the following chunk of sentences.

It gives both coarse-grained labels (like authors, title, venue, date, etc.)
and fine-grained labels (e.g., booktitle, address, volume, etc.
of a venue) for each citation string.
In the first step, characters, words, lines, blocks, etc.
are extracted from PDF files using an OCR software (Nuance Omnipage).
Grobid [21] is another powerful tool that breaks down PDFs into several LTBs, like title, abstract, sections
using Conditional Random Fields.
Detailed characteristics of the test dataset are shown in NAME_1.
4.
As for the ML models, we deliberately set up a rather expen- sive object-detection network (Faster R-CNN with ResNet50 backbone, ~ 63M weights) [25] for layout segmentation, which we feed PDF pages rasterized to 1025×1025 pixels.
Those parameters directly influence the quality of segmentation,
in paragraph, and are difficult to determine, in particular the nominal line spacing that may vary across pages or within pages.
We propose to view this problem as an optimization problem, i.e.
what is the best series of cuts in view of some optimization criterion.
Figure 4 shows on the left hand three possible horizontal cuts (A.
B, C) defining four possible blocks (1,2,3,4).
The last two years have seen the rise of Transfer Learning approaches in 
Natural Language Processing (NLP) with large-scale pre-trained language models becoming a basic tool in many NLP tasks [NAME_2 NAME_3 NAME_4., 2018, NAME_5 NAME_3 NAME_4., 2019, Liu NAME_3 NAME_4., 2019]..
Image Transformer has recently achieved signiﬁcant progress for natural image understanding, either using supervised (ViT, DeiT, etc.)
or self-supervised (BEiT, MAE, etc.)
pre-training techniques.
Self-supervised pre-training techniques have been the de facto common practice for Document AI (Cui NAME_3 NAME_4., 2021) in the past several years, where the image, text and layout information is often jointly trained using a uniﬁed Transformer architecture (Xu NAME_3 NAME_4., 2020; 2021a;b; NAME_6 NAME_3 NAME_4., 2020; NAME_7 NAME_3 NAME_4., 2021; NAME_8 NAME_4., 2021; NAME_9 NAME_3 NAME_4., 2021; NAME_10 NAME_3 NAME_4., 2021; NAME_11 NAME_4., 2021a;b; Appalaraju NAME_3 NAME_4., 2021).
An input text image is ﬁrst resized into 224 × 224 and then the image is split into a sequence of 16 × 16 patches which are used as the input to the image Transformer.
Following ViT (NAME_12 NAME_3 NAME_4., 2021), we use the vanilla Transformer architecture (NAME_13 NAME_3 NAME_4., 2017) as the backbone of DiT.
We divide a document image into non-overlapping patches and obtain a sequence of patch embeddings.
Doc- former: End-to-end transfo The words with all capital letters should appear at least 2 times.
user