!pip install pypdf
 

from langchain.document_loaders import PyPDFLoader
 
loader = PyPDFLoader("example_data/layout-parser-paper.pdf")
pages = loader.load_and_split()
 

pages[0]
 

Document(page_content='LayoutParser : A Unified Toolkit for Deep  Learning Based Document Image Analysis  Zejiang Shen1( \x00), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain  Lee4, Jacob Carlson3, and Weining Li5  1Allen Institute for AI  shannons@allenai.org  2Brown University  ruochen zhang@brown.edu  3Harvard University  fmelissadell,jacob carlson g@fas.harvard.edu  4University of Washington  bcgl@cs.washington.edu  5University of Waterloo  w422li@uwaterloo.ca  Abstract. Recent advances in document image analysis (DIA) have been  primarily driven by the application of neural networks. Ideally, research  outcomes could be easily deployed in production and extended for further  investigation. However, various factors like loosely organized codebases  and sophisticated model con\x0cgurations complicate the easy reuse of im-  portant innovations by a wide audience. Though there have been on-going  e\x0borts to improve reusability and simplify deep learning (DL) model  development in disciplines like natural language processing and computer  vision, none of them are optimized for challenges in the domain of DIA.  This represents a major gap in the existing toolkit, as DIA is central to  academic research across a wide range of disciplines in the social sciences  and humanities. This paper introduces LayoutParser , an open-source  library for streamlining the usage of DL in DIA research and applica-  tions. The core LayoutParser library comes with a set of simple and  intuitive interfaces for applying and customizing DL models for layout de-  tection, character recognition, and many other document processing tasks.  To promote extensibility, LayoutParser also incorporates a community  platform for sharing both pre-trained models and full document digiti-  zation pipelines. We demonstrate that LayoutParser is helpful for both  lightweight and large-scale digitization pipelines in real-word use cases.  The library is publicly available at https://layout-parser.github.io .  Keywords: Document Image Analysis ·Deep Learning ·Layout Analysis  ·Character Recognition ·Open Source library ·Toolkit.  1 Introduction  Deep Learning(DL)-based approaches are the state-of-the-art for a wide range of  document image analysis (DIA) tasks including document image classi\x0ccation [ 11,arXiv:2103.15348v2  [cs.CV]  21 Jun 2021', metadata={'source': 'example_data/layout-parser-paper.pdf', 'page': 0})
 

import os
import getpass
 
os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')
 

from langchain.vectorstores import FAISS
from langchain.embeddings.openai import OpenAIEmbeddings
 
faiss_index = FAISS.from_documents(pages, OpenAIEmbeddings())
docs = faiss_index.similarity_search("How will the community be engaged?", k=2)
for doc in docs:
    print(str(doc.metadata["page"]) + ":", doc.page_content[:300])
 

9: 10 Z. Shen et al.
Fig. 4: Illustration of (a) the original historical Japanese document with layout
detection results and (b) a recreated version of the document image that achieves
much better character recognition recall. The reorganization algorithm rearranges
the tokens based on the their detect
3: 4 Z. Shen et al.
Efficient Data AnnotationC u s t o m i z e d  M o d e l  T r a i n i n gModel Cust omizationDI A Model HubDI A Pipeline SharingCommunity PlatformLa y out Detection ModelsDocument Images 
T h e  C o r e  L a y o u t P a r s e r  L i b r a r yOCR ModuleSt or age & VisualizationLa y ou
 

from langchain.document_loaders import MathpixPDFLoader
 

loader = MathpixPDFLoader("example_data/layout-parser-paper.pdf")
 

data = loader.load()
 

from langchain.document_loaders import UnstructuredPDFLoader
 

loader = UnstructuredPDFLoader("example_data/layout-parser-paper.pdf")
 

data = loader.load()
 

loader = UnstructuredPDFLoader("example_data/layout-parser-paper.pdf", mode="elements")
 

data = loader.load()
 

data[0]
 

Document(page_content='LayoutParser: A Unified Toolkit for Deep  Learning Based Document Image Analysis  Zejiang Shen1 (�), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain  Lee4, Jacob Carlson3, and Weining Li5  1 Allen Institute for AI  shannons@allenai.org  2 Brown University  ruochen zhang@brown.edu  3 Harvard University  {melissadell,jacob carlson}@fas.harvard.edu  4 University of Washington  bcgl@cs.washington.edu  5 University of Waterloo  w422li@uwaterloo.ca  Abstract. Recent advances in document image analysis (DIA) have been  primarily driven by the application of neural networks. Ideally, research  outcomes could be easily deployed in production and extended for further  investigation. However, various factors like loosely organized codebases  and sophisticated model configurations complicate the easy reuse of im-  portant innovations by a wide audience. Though there have been on-going  efforts to improve reusability and simplify deep learning (DL) model  development in disciplines like natural language processing and computer  vision, none of them are optimized for challenges in the domain of DIA.  This represents a major gap in the existing toolkit, as DIA is central to  academic research across a wide range of disciplines in the social sciences  and humanities. This paper introduces LayoutParser, an open-source  library for streamlining the usage of DL in DIA research and applica-  tions. The core LayoutParser library comes with a set of simple and  intuitive interfaces for applying and customizing DL models for layout de-  tection, character recognition, and many other document processing tasks.  To promote extensibility, LayoutParser also incorporates a community  platform for sharing both pre-trained models and full document digiti-  zation pipelines. We demonstrate that LayoutParser is helpful for both  lightweight and large-scale digitization pipelines in real-word use cases.  The library is publicly available at https://layout-parser.github.io.  Keywords: Document Image Analysis · Deep Learning · Layout Analysis  · Character Recognition · Open Source library · Toolkit.  1  Introduction  Deep Learning(DL)-based approaches are the state-of-the-art for a wide range of  document image analysis (DIA) tasks including document image classification [11,  arXiv:2103.15348v2  [cs.CV]  21 Jun 2021  ', lookup_str='', metadata={'file_path': 'example_data/layout-parser-paper.pdf', 'page_number': 1, 'total_pages': 16, 'format': 'PDF 1.5', 'title': '', 'author': '', 'subject': '', 'keywords': '', 'creator': 'LaTeX with hyperref', 'producer': 'pdfTeX-1.40.21', 'creationDate': 'D:20210622012710Z', 'modDate': 'D:20210622012710Z', 'trapped': '', 'encryption': None}, lookup_index=0)
 

from langchain.document_loaders import OnlinePDFLoader
 

loader = OnlinePDFLoader("https://arxiv.org/pdf/2302.03803.pdf")
 

data = loader.load()
 

print(data)
 

[Document(page_content='A WEAK ( k, k ) -LEFSCHETZ THEOREM FOR PROJECTIVE TORIC ORBIFOLDS  William D. Montoya  Instituto de Matem´atica, Estat´ıstica e Computa¸c˜ao Cient´ıfica,  In [3] we proved that, under suitable conditions, on a very general codimension s quasi- smooth intersection subvariety X in a projective toric orbifold P d Σ with d + s = 2 ( k + 1 ) the Hodge conjecture holds, that is, every ( p, p ) -cohomology class, under the Poincar´e duality is a rational linear combination of fundamental classes of algebraic subvarieties of X . The proof of the above-mentioned result relies, for p ≠ d + 1 − s , on a Lefschetz  Keywords: (1,1)- Lefschetz theorem, Hodge conjecture, toric varieties, complete intersection Email: wmontoya@ime.unicamp.br  theorem ([7]) and the Hard Lefschetz theorem for projective orbifolds ([11]). When p = d + 1 − s the proof relies on the Cayley trick, a trick which associates to X a quasi-smooth hypersurface Y in a projective vector bundle, and the Cayley Proposition (4.3) which gives an isomorphism of some primitive cohomologies (4.2) of X and Y . The Cayley trick, following the philosophy of Mavlyutov in [7], reduces results known for quasi-smooth hypersurfaces to quasi-smooth intersection subvarieties. The idea in this paper goes the other way around, we translate some results for quasi-smooth intersection subvarieties to  Acknowledgement. I thank Prof. Ugo Bruzzo and Tiago Fonseca for useful discus- sions. I also acknowledge support from FAPESP postdoctoral grant No. 2019/23499-7.  Let M be a free abelian group of rank d , let N = Hom ( M, Z ) , and N R = N ⊗ Z R .  if there exist k linearly independent primitive elements e  , . . . , e k ∈ N such that σ = { µ  e  + ⋯ + µ k e k } . • The generators e i are integral if for every i and any nonnegative rational number µ the product µe i is in N only if µ is an integer. • Given two rational simplicial cones σ , σ ′ one says that σ ′ is a face of σ ( σ ′ < σ ) if the set of integral generators of σ ′ is a subset of the set of integral generators of σ . • A finite set Σ = { σ  , . . . , σ t } of rational simplicial cones is called a rational simplicial complete d -dimensional fan if:  all faces of cones in Σ are in Σ ;  if σ, σ ′ ∈ Σ then σ ∩ σ ′ < σ and σ ∩ σ ′ < σ ′ ;  N R = σ  ∪ ⋅ ⋅ ⋅ ∪ σ t .  A rational simplicial complete d -dimensional fan Σ defines a d -dimensional toric variety P d Σ having only orbifold singularities which we assume to be projective. Moreover, T ∶ = N ⊗ Z C ∗ ≃ ( C ∗ ) d is the torus action on P d Σ . We denote by Σ ( i ) the i -dimensional cones  For a cone σ ∈ Σ, ˆ σ is the set of 1-dimensional cone in Σ that are not contained in σ  and x ˆ σ ∶ = ∏ ρ ∈ ˆ σ x ρ is the associated monomial in S .  Definition 2.2. The irrelevant ideal of P d Σ is the monomial ideal B Σ ∶ =< x ˆ σ ∣ σ ∈ Σ > and the zero locus Z ( Σ ) ∶ = V ( B Σ ) in the affine space A d ∶ = Spec ( S ) is the irrelevant locus.  Proposition 2.3 (Theorem 5.1.11 [5]) . The toric variety P d Σ is a categorical quotient A d ∖ Z ( Σ ) by the group Hom ( Cl ( Σ ) , C ∗ ) and the group action is induced by the Cl ( Σ ) - grading of S .  Now we give a brief introduction to complex orbifolds and we mention the needed theorems for the next section. Namely: de Rham theorem and Dolbeault theorem for complex orbifolds.  Definition 2.4. A complex orbifold of complex dimension d is a singular complex space whose singularities are locally isomorphic to quotient singularities C d / G , for finite sub- groups G ⊂ Gl ( d, C ) .  Definition 2.5. A differential form on a complex orbifold Z is defined locally at z ∈ Z as a G -invariant differential form on C d where G ⊂ Gl ( d, C ) and Z is locally isomorphic to d  Roughly speaking the local geometry of orbifolds reduces to local G -invariant geometry.  We have a complex of differential forms ( A ● ( Z ) , d ) and a double complex ( A ● , ● ( Z ) , ∂, ¯ ∂ ) of bigraded differential forms which define the de Rham and the Dolbeault cohomology groups (for a fixed p ∈ N ) respectively:  (1,1)-Lefschetz theorem for projective toric orbifolds  Definition 3.1. A subvariety X ⊂ P d Σ is quasi-smooth if V ( I X ) ⊂ A #Σ ( 1 ) is smooth outside  Example 3.2 . Quasi-smooth hypersurfaces or more generally quasi-smooth intersection sub-  Example 3.2 . Quasi-smooth hypersurfaces or more generally quasi-smooth intersection sub- varieties are quasi-smooth subvarieties (see [2] or [7] for more details).  Remark 3.3 . Quasi-smooth subvarieties are suborbifolds of P d Σ in the sense of Satake in [8]. Intuitively speaking they are subvarieties whose only singularities come from the ambient  Proof. From the exponential short exact sequence  we have a long exact sequence in cohomology  H 1 (O ∗ X ) → H 2 ( X, Z ) → H 2 (O X ) ≃ H 0 , 2 ( X )  where the last isomorphisms is due to Steenbrink in [9]. Now, it is enough to prove the commutativity of the next diagram  where the last isomorphisms is due to Steenbrink in [9]. Now,  H 2 ( X, Z ) / / H 2 ( X, O X ) ≃ Dolbeault H 2 ( X, C ) deRham ≃ H 2 dR ( X, C ) / / H 0 , 2 ¯ ∂ ( X )  of the proof follows as the ( 1 , 1 ) -Lefschetz theorem in [6].  Remark 3.5 . For k = 1 and P d Σ as the projective space, we recover the classical ( 1 , 1 ) - Lefschetz theorem.  By the Hard Lefschetz Theorem for projective orbifolds (see [11] for details) we  By the Hard Lefschetz Theorem for projective orbifolds (see [11] for details) we get an isomorphism of cohomologies :  given by the Lefschetz morphism and since it is a morphism of Hodge structures, we have:  H 1 , 1 ( X, Q ) ≃ H dim X − 1 , dim X − 1 ( X, Q )  Corollary 3.6. If the dimension of X is 1 , 2 or 3 . The Hodge conjecture holds on X  Proof. If the dim C X = 1 the result is clear by the Hard Lefschetz theorem for projective orbifolds. The dimension 2 and 3 cases are covered by Theorem 3.5 and the Hard Lefschetz.  Cayley trick and Cayley proposition  The Cayley trick is a way to associate to a quasi-smooth intersection subvariety a quasi- smooth hypersurface. Let L 1 , . . . , L s be line bundles on P d Σ and let π ∶ P ( E ) → P d Σ be the projective space bundle associated to the vector bundle E = L 1 ⊕ ⋯ ⊕ L s . It is known that P ( E ) is a ( d + s − 1 ) -dimensional simplicial toric variety whose fan depends on the degrees of the line bundles and the fan Σ. Furthermore, if the Cox ring, without considering the grading, of P d Σ is C [ x 1 , . . . , x m ] then the Cox ring of P ( E ) is  Moreover for X a quasi-smooth intersection subvariety cut off by f 1 , . . . , f s with deg ( f i ) = [ L i ] we relate the hypersurface Y cut off by F = y 1 f 1 + ⋅ ⋅ ⋅ + y s f s which turns out to be quasi-smooth. For more details see Section 2 in [7].  We will denote P ( E ) as P d + s − 1 Σ ,X to keep track of its relation with X and P d Σ .  The following is a key remark.  Remark 4.1 . There is a morphism ι ∶ X → Y ⊂ P d + s − 1 Σ ,X . Moreover every point z ∶ = ( x, y ) ∈ Y with y ≠ 0 has a preimage. Hence for any subvariety W = V ( I W ) ⊂ X ⊂ P d Σ there exists W ′ ⊂ Y ⊂ P d + s − 1 Σ ,X such that π ( W ′ ) = W , i.e., W ′ = { z = ( x, y ) ∣ x ∈ W } .  For X ⊂ P d Σ a quasi-smooth intersection variety the morphism in cohomology induced by the inclusion i ∗ ∶ H d − s ( P d Σ , C ) → H d − s ( X, C ) is injective by Proposition 1.4 in [7].  Definition 4.2. The primitive cohomology of H d − s prim ( X ) is the quotient H d − s ( X, C )/ i ∗ ( H d − s ( P d Σ , C )) and H d − s prim ( X, Q ) with rational coefficients.  H d − s ( P d Σ , C ) and H d − s ( X, C ) have pure Hodge structures, and the morphism i ∗ is com- patible with them, so that H d − s prim ( X ) gets a pure Hodge structure.  The next Proposition is the Cayley proposition.  Proposition 4.3. [Proposition 2.3 in [3] ] Let X = X 1 ∩⋅ ⋅ ⋅∩ X s be a quasi-smooth intersec- tion subvariety in P d Σ cut off by homogeneous polynomials f 1 . . . f s . Then for p ≠ d + s − 1 2 , d + s − 3 2  Remark 4.5 . The above isomorphisms are also true with rational coefficients since H ● ( X, C ) = H ● ( X, Q ) ⊗ Q C . See the beginning of Section 7.1 in [10] for more details.  Theorem 5.1. Let Y = { F = y 1 f 1 + ⋯ + y k f k = 0 } ⊂ P 2 k + 1 Σ ,X be the quasi-smooth hypersurface associated to the quasi-smooth intersection surface X = X f 1 ∩ ⋅ ⋅ ⋅ ∩ X f k ⊂ P k + 2 Σ . Then on Y the Hodge conjecture holds.  the Hodge conjecture holds.  Proof. If H k,k prim ( X, Q ) = 0 we are done. So let us assume H k,k prim ( X, Q ) ≠ 0. By the Cayley proposition H k,k prim ( Y, Q ) ≃ H 1 , 1 prim ( X, Q ) and by the ( 1 , 1 ) -Lefschetz theorem for projective  toric orbifolds there is a non-zero algebraic basis λ C 1 , . . . , λ C n with rational coefficients of H 1 , 1 prim ( X, Q ) , that is, there are n ∶ = h 1 , 1 prim ( X, Q ) algebraic curves C 1 , . . . , C n in X such that under the Poincar´e duality the class in homology [ C i ] goes to λ C i , [ C i ] ↦ λ C i . Recall that the Cox ring of P k + 2 is contained in the Cox ring of P 2 k + 1 Σ ,X without considering the grading. Considering the grading we have that if α ∈ Cl ( P k + 2 Σ ) then ( α, 0 ) ∈ Cl ( P 2 k + 1 Σ ,X ) . So the polynomials defining C i ⊂ P k + 2 Σ can be interpreted in P 2 k + 1 X, Σ but with different degree. Moreover, by Remark 4.1 each C i is contained in Y = { F = y 1 f 1 + ⋯ + y k f k = 0 } and  furthermore it has codimension k .  Claim: { C i } ni = 1 is a basis of prim ( ) . It is enough to prove that λ C i is different from zero in H k,k prim ( Y, Q ) or equivalently that the cohomology classes { λ C i } ni = 1 do not come from the ambient space. By contradiction, let us assume that there exists a j and C ⊂ P 2 k + 1 Σ ,X such that λ C ∈ H k,k ( P 2 k + 1 Σ ,X , Q ) with i ∗ ( λ C ) = λ C j or in terms of homology there exists a ( k + 2 ) -dimensional algebraic subvariety V ⊂ P 2 k + 1 Σ ,X such that V ∩ Y = C j so they are equal as a homology class of P 2 k + 1 Σ ,X ,i.e., [ V ∩ Y ] = [ C j ] . It is easy to check that π ( V ) ∩ X = C j as a subvariety of P k + 2 Σ where π ∶ ( x, y ) ↦ x . Hence [ π ( V ) ∩ X ] = [ C j ] which is equivalent to say that λ C j comes from P k + 2 Σ which contradicts the choice of [ C j ] .  Remark 5.2 . Into the proof of the previous theorem, the key fact was that on X the Hodge conjecture holds and we translate it to Y by contradiction. So, using an analogous argument we have:  argument we have:  Proposition 5.3. Let Y = { F = y 1 f s +⋯+ y s f s = 0 } ⊂ P 2 k + 1 Σ ,X be the quasi-smooth hypersurface associated to a quasi-smooth intersection subvariety X = X f 1 ∩ ⋅ ⋅ ⋅ ∩ X f s ⊂ P d Σ such that d + s = 2 ( k + 1 ) . If the Hodge conjecture holds on X then it holds as well on Y .  Corollary 5.4. If the dimension of Y is 2 s − 1 , 2 s or 2 s + 1 then the Hodge conjecture holds on Y .  Proof. By Proposition 5.3 and Corollary 3.6.  [  ] Angella, D. Cohomologies of certain orbifolds. Journal of Geometry and Physics  (  ),  –  [  ] Batyrev, V. V., and Cox, D. A. On the Hodge structure of projective hypersur- faces in toric varieties. Duke Mathematical Journal  ,  (Aug  ). [  ] Bruzzo, U., and Montoya, W. On the Hodge conjecture for quasi-smooth in- tersections in toric varieties. S˜ao Paulo J. Math. Sci. Special Section: Geometry in Algebra and Algebra in Geometry (  ). [  ] Caramello Jr, F. C. Introduction to orbifolds. a  iv:  v  (  ). [  ] Cox, D., Little, J., and Schenck, H. Toric varieties, vol.  American Math- ematical Soc.,  [  ] Griffiths, P., and Harris, J. Principles of Algebraic Geometry. John Wiley & Sons, Ltd,  [  ] Mavlyutov, A. R. Cohomology of complete intersections in toric varieties. Pub- lished in Pacific J. of Math.  No.  (  ),  –  [  ] Satake, I. On a Generalization of the Notion of Manifold. Proceedings of the National Academy of Sciences of the United States of America  ,  (  ),  –  [  ] Steenbrink, J. H. M. Intersection form for quasi-homogeneous singularities. Com- positio Mathematica  ,  (  ),  –  [  ] Voisin, C. Hodge Theory and Complex Algebraic Geometry I, vol.  of Cambridge Studies in Advanced Mathematics . Cambridge University Press,  [  ] Wang, Z. Z., and Zaffran, D. A remark on the Hard Lefschetz theorem for K¨ahler orbifolds. Proceedings of the American Mathematical Society  ,  (Aug  ).  [2] Batyrev, V. V., and Cox, D. A. On the Hodge structure of projective hypersur- faces in toric varieties. Duke Mathematical Journal 75, 2 (Aug 1994).  [  ] Bruzzo, U., and Montoya, W. On the Hodge conjecture for quasi-smooth in- tersections in toric varieties. S˜ao Paulo J. Math. Sci. Special Section: Geometry in Algebra and Algebra in Geometry (  ).  [3] Bruzzo, U., and Montoya, W. On the Hodge conjecture for quasi-smooth in- tersections in toric varieties. S˜ao Paulo J. Math. Sci. Special Section: Geometry in Algebra and Algebra in Geometry (2021).  A. R. Cohomology of complete intersections in toric varieties. Pub-', lookup_str='', metadata={'source': '/var/folders/ph/hhm7_zyx4l13k3v8z02dwp1w0000gn/T/tmpgq0ckaja/online_file.pdf'}, lookup_index=0)]
 

from langchain.document_loaders import PDFMinerLoader
 

loader = PDFMinerLoader("example_data/layout-parser-paper.pdf")
 

data = loader.load()
 

from langchain.document_loaders import PyPDFium2Loader
 

loader = PyPDFium2Loader("example_data/layout-parser-paper.pdf")
 

data = loader.load()
 

from langchain.document_loaders import PDFMinerPDFasHTMLLoader
 

loader = PDFMinerPDFasHTMLLoader("example_data/layout-parser-paper.pdf")
 

data = loader.load()[0]   # entire pdf is loaded as a single Document
 

from bs4 import BeautifulSoup
soup = BeautifulSoup(data.page_content,'html.parser')
content = soup.find_all('div')
 

import re
cur_fs = None
cur_text = ''
snippets = []   # first collect all snippets that have the same font size
for c in content:
    sp = c.find('span')
    if not sp:
        continue
    st = sp.get('style')
    if not st:
        continue
    fs = re.findall('font-size:(\d+)px',st)
    if not fs:
        continue
    fs = int(fs[0])
    if not cur_fs:
        cur_fs = fs
    if fs == cur_fs:
        cur_text += c.text
    else:
        snippets.append((cur_text,cur_fs))
        cur_fs = fs
        cur_text = c.text
snippets.append((cur_text,cur_fs))
# Note: The above logic is very straightforward. One can also add more strategies such as removing duplicate snippets (as
# headers/footers in a PDF appear on multiple pages so if we find duplicatess safe to assume that it is redundant info)
 

from langchain.docstore.document import Document
cur_idx = -1
semantic_snippets = []
# Assumption: headings have higher font size than their respective content
for s in snippets:
    # if current snippet's font size > previous section's heading => it is a new heading
    if not semantic_snippets or s[1] > semantic_snippets[cur_idx].metadata['heading_font']:
        metadata={'heading':s[0], 'content_font': 0, 'heading_font': s[1]}
        metadata.update(data.metadata)
        semantic_snippets.append(Document(page_content='',metadata=metadata))
        cur_idx += 1
        continue
 
    # if current snippet's font size <= previous section's content => content belongs to the same section (one can also create
    # a tree like structure for sub sections if needed but that may require some more thinking and may be data specific)
    if not semantic_snippets[cur_idx].metadata['content_font'] or s[1] <= semantic_snippets[cur_idx].metadata['content_font']:
        semantic_snippets[cur_idx].page_content += s[0]
        semantic_snippets[cur_idx].metadata['content_font'] = max(s[1], semantic_snippets[cur_idx].metadata['content_font'])
        continue
 
    # if current snippet's font size > previous section's content but less tha previous section's heading than also make a new 
    # section (e.g. title of a pdf will have the highest font size but we don't want it to subsume all sections)
    metadata={'heading':s[0], 'content_font': 0, 'heading_font': s[1]}
    metadata.update(data.metadata)
    semantic_snippets.append(Document(page_content='',metadata=metadata))
    cur_idx += 1
 

semantic_snippets[4]
 

Document(page_content='Recently, various DL models and datasets have been developed for layout analysis  tasks. The dhSegment [22] utilizes fully convolutional networks [20] for segmen-  tation tasks on historical documents. Object detection-based methods like Faster  R-CNN [28] and Mask R-CNN [12] are used for identifying document elements [38]  and detecting tables [30, 26]. Most recently, Graph Neural Networks [29] have also  been used in table detection [27]. However, these models are usually implemented  individually and there is no unified framework to load and use such models.  There has been a surge of interest in creating open-source tools for document  image processing: a search of document image analysis in Github leads to 5M  relevant code pieces 6; yet most of them rely on traditional rule-based methods  or provide limited functionalities. The closest prior research to our work is the  OCR-D project7, which also tries to build a complete toolkit for DIA. However,  similar to the platform developed by Neudecker et al. [21], it is designed for  analyzing historical documents, and provides no supports for recent DL models.  The DocumentLayoutAnalysis project8 focuses on processing born-digital PDF  documents via analyzing the stored PDF data. Repositories like DeepLayout9  and Detectron2-PubLayNet10 are individual deep learning models trained on  layout analysis datasets without support for the full DIA pipeline. The Document  Analysis and Exploitation (DAE) platform [15] and the DeepDIVA project [2]  aim to improve the reproducibility of DIA methods (or DL models), yet they  are not actively maintained. OCR engines like Tesseract [14], easyOCR11 and  paddleOCR12 usually do not come with comprehensive functionalities for other  DIA tasks like layout analysis.  Recent years have also seen numerous efforts to create libraries for promoting  reproducibility and reusability in the field of DL. Libraries like Dectectron2 [35],  6 The number shown is obtained by specifying the search type as ‘code’.  7 https://ocr-d.de/en/about  8 https://github.com/BobLd/DocumentLayoutAnalysis  9 https://github.com/leonlulu/DeepLayout  10 https://github.com/hpanwar08/detectron2  11 https://github.com/JaidedAI/EasyOCR  12 https://github.com/PaddlePaddle/PaddleOCR  4  Z. Shen et al.  Fig. 1: The overall architecture of LayoutParser. For an input document image,  the core LayoutParser library provides a set of off-the-shelf tools for layout  detection, OCR, visualization, and storage, backed by a carefully designed layout  data structure. LayoutParser also supports high level customization via efficient  layout annotation and model training functions. These improve model accuracy  on the target samples. The community platform enables the easy sharing of DIA  models and whole digitization pipelines to promote reusability and reproducibility.  A collection of detailed documentation, tutorials and exemplar projects make  LayoutParser easy to learn and use.  AllenNLP [8] and transformers [34] have provided the community with complete  DL-based support for developing and deploying models for general computer  vision and natural language processing problems. LayoutParser, on the other  hand, specializes specifically in DIA tasks. LayoutParser is also equipped with a  community platform inspired by established model hubs such as Torch Hub [23]  and TensorFlow Hub [1]. It enables the sharing of pretrained models as well as  full document processing pipelines that are unique to DIA tasks.  There have been a variety of document data collections to facilitate the  development of DL models. Some examples include PRImA [3](magazine layouts),  PubLayNet [38](academic paper layouts), Table Bank [18](tables in academic  papers), Newspaper Navigator Dataset [16, 17](newspaper figure layouts) and  HJDataset [31](historical Japanese document layouts). A spectrum of models  trained on these datasets are currently available in the LayoutParser model zoo  to support different use cases.  ', metadata={'heading': '2 Related Work  ', 'content_font': 9, 'heading_font': 11, 'source': 'example_data/layout-parser-paper.pdf'})
 

from langchain.document_loaders import PyMuPDFLoader
 

loader = PyMuPDFLoader("example_data/layout-parser-paper.pdf")
 

data = loader.load()
 

data[0]
 

Document(page_content='LayoutParser: A Unified Toolkit for Deep  Learning Based Document Image Analysis  Zejiang Shen1 (�), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain  Lee4, Jacob Carlson3, and Weining Li5  1 Allen Institute for AI  shannons@allenai.org  2 Brown University  ruochen zhang@brown.edu  3 Harvard University  {melissadell,jacob carlson}@fas.harvard.edu  4 University of Washington  bcgl@cs.washington.edu  5 University of Waterloo  w422li@uwaterloo.ca  Abstract. Recent advances in document image analysis (DIA) have been  primarily driven by the application of neural networks. Ideally, research  outcomes could be easily deployed in production and extended for further  investigation. However, various factors like loosely organized codebases  and sophisticated model configurations complicate the easy reuse of im-  portant innovations by a wide audience. Though there have been on-going  efforts to improve reusability and simplify deep learning (DL) model  development in disciplines like natural language processing and computer  vision, none of them are optimized for challenges in the domain of DIA.  This represents a major gap in the existing toolkit, as DIA is central to  academic research across a wide range of disciplines in the social sciences  and humanities. This paper introduces LayoutParser, an open-source  library for streamlining the usage of DL in DIA research and applica-  tions. The core LayoutParser library comes with a set of simple and  intuitive interfaces for applying and customizing DL models for layout de-  tection, character recognition, and many other document processing tasks.  To promote extensibility, LayoutParser also incorporates a community  platform for sharing both pre-trained models and full document digiti-  zation pipelines. We demonstrate that LayoutParser is helpful for both  lightweight and large-scale digitization pipelines in real-word use cases.  The library is publicly available at https://layout-parser.github.io.  Keywords: Document Image Analysis · Deep Learning · Layout Analysis  · Character Recognition · Open Source library · Toolkit.  1  Introduction  Deep Learning(DL)-based approaches are the state-of-the-art for a wide range of  document image analysis (DIA) tasks including document image classification [11,  arXiv:2103.15348v2  [cs.CV]  21 Jun 2021  ', lookup_str='', metadata={'file_path': 'example_data/layout-parser-paper.pdf', 'page_number': 1, 'total_pages': 16, 'format': 'PDF 1.5', 'title': '', 'author': '', 'subject': '', 'keywords': '', 'creator': 'LaTeX with hyperref', 'producer': 'pdfTeX-1.40.21', 'creationDate': 'D:20210622012710Z', 'modDate': 'D:20210622012710Z', 'trapped': '', 'encryption': None}, lookup_index=0)
 

from langchain.document_loaders import PyPDFDirectoryLoader
 

loader = PyPDFDirectoryLoader("example_data")
 

docs = loader.load()