mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
220a7076ac
Inspo https://twitter.com/danielgross/status/1651695062307274754?s=46&t=1zHLap5WG4I_kQPPjfW9fA Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
680 lines
45 KiB
Plaintext
680 lines
45 KiB
Plaintext
{
|
||
"cells": [
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "f70e6118",
|
||
"metadata": {},
|
||
"source": [
|
||
"# PDF\n",
|
||
"\n",
|
||
"This covers how to load pdfs into a document format that we can use downstream."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "743f9413",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Using PyPDF\n",
|
||
"\n",
|
||
"Load PDF using `pypdf` into array of documents, where each document contains the page content and metadata with `page` number."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 1,
|
||
"id": "c428b0c5",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from langchain.document_loaders import PyPDFLoader\n",
|
||
"\n",
|
||
"loader = PyPDFLoader(\"example_data/layout-parser-paper.pdf\")\n",
|
||
"pages = loader.load_and_split()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"id": "d333cabb",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Document(page_content='LayoutParser : A Uni\\x0ced Toolkit for Deep\\nLearning Based Document Image Analysis\\nZejiang Shen1( \\x00), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain\\nLee4, Jacob Carlson3, and Weining Li5\\n1Allen Institute for AI\\nshannons@allenai.org\\n2Brown University\\nruochen zhang@brown.edu\\n3Harvard University\\nfmelissadell,jacob carlson g@fas.harvard.edu\\n4University of Washington\\nbcgl@cs.washington.edu\\n5University of Waterloo\\nw422li@uwaterloo.ca\\nAbstract. Recent advances in document image analysis (DIA) have been\\nprimarily driven by the application of neural networks. Ideally, research\\noutcomes could be easily deployed in production and extended for further\\ninvestigation. However, various factors like loosely organized codebases\\nand sophisticated model con\\x0cgurations complicate the easy reuse of im-\\nportant innovations by a wide audience. Though there have been on-going\\ne\\x0borts to improve reusability and simplify deep learning (DL) model\\ndevelopment in disciplines like natural language processing and computer\\nvision, none of them are optimized for challenges in the domain of DIA.\\nThis represents a major gap in the existing toolkit, as DIA is central to\\nacademic research across a wide range of disciplines in the social sciences\\nand humanities. This paper introduces LayoutParser , an open-source\\nlibrary for streamlining the usage of DL in DIA research and applica-\\ntions. The core LayoutParser library comes with a set of simple and\\nintuitive interfaces for applying and customizing DL models for layout de-\\ntection, character recognition, and many other document processing tasks.\\nTo promote extensibility, LayoutParser also incorporates a community\\nplatform for sharing both pre-trained models and full document digiti-\\nzation pipelines. We demonstrate that LayoutParser is helpful for both\\nlightweight and large-scale digitization pipelines in real-word use cases.\\nThe library is publicly available at https://layout-parser.github.io .\\nKeywords: Document Image Analysis ·Deep Learning ·Layout Analysis\\n·Character Recognition ·Open Source library ·Toolkit.\\n1 Introduction\\nDeep Learning(DL)-based approaches are the state-of-the-art for a wide range of\\ndocument image analysis (DIA) tasks including document image classi\\x0ccation [ 11,arXiv:2103.15348v2 [cs.CV] 21 Jun 2021', lookup_str='', metadata={'source': 'example_data/layout-parser-paper.pdf', 'page': '0'}, lookup_index=0)"
|
||
]
|
||
},
|
||
"execution_count": 4,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"pages[0]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "ebd895e4",
|
||
"metadata": {},
|
||
"source": [
|
||
"An advantage of this approach is that documents can be retrieved with page numbers."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "87fa7b3a",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"9: 10 Z. Shen et al.\n",
|
||
"Fig. 4: Illustration of (a) the original historical Japanese document with layout\n",
|
||
"detection results and (b) a recreated version of the document image that achieves\n",
|
||
"much better character recognition recall. The reorganization algorithm rearranges\n",
|
||
"the tokens based on the their detected bounding boxes given a maximum allowed\n",
|
||
"height.\n",
|
||
"4LayoutParser Community Platform\n",
|
||
"Another focus of LayoutParser is promoting the reusability of layout detection\n",
|
||
"models and full digitization pipelines. Similar to many existing deep learning\n",
|
||
"libraries, LayoutParser comes with a community model hub for distributing\n",
|
||
"layout models. End-users can upload their self-trained models to the model hub,\n",
|
||
"and these models can be loaded into a similar interface as the currently available\n",
|
||
"LayoutParser pre-trained models. For example, the model trained on the News\n",
|
||
"Navigator dataset [17] has been incorporated in the model hub.\n",
|
||
"Beyond DL models, LayoutParser also promotes the sharing of entire doc-\n",
|
||
"ument digitization pipelines. For example, sometimes the pipeline requires the\n",
|
||
"combination of multiple DL models to achieve better accuracy. Currently, pipelines\n",
|
||
"are mainly described in academic papers and implementations are often not pub-\n",
|
||
"licly available. To this end, the LayoutParser community platform also enables\n",
|
||
"the sharing of layout pipelines to promote the discussion and reuse of techniques.\n",
|
||
"For each shared pipeline, it has a dedicated project page, with links to the source\n",
|
||
"code, documentation, and an outline of the approaches. A discussion panel is\n",
|
||
"provided for exchanging ideas. Combined with the core LayoutParser library,\n",
|
||
"users can easily build reusable components based on the shared pipelines and\n",
|
||
"apply them to solve their unique problems.\n",
|
||
"5 Use Cases\n",
|
||
"The core objective of LayoutParser is to make it easier to create both large-scale\n",
|
||
"and light-weight document digitization pipelines. Large-scale document processing\n",
|
||
"3: 4 Z. Shen et al.\n",
|
||
"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 \n",
|
||
"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 out Data Structur e\n",
|
||
"Fig. 1: The overall architecture of LayoutParser . For an input document image,\n",
|
||
"the core LayoutParser library provides a set of o\u000b",
|
||
"\n",
|
||
"-the-shelf tools for layout\n",
|
||
"detection, OCR, visualization, and storage, backed by a carefully designed layout\n",
|
||
"data structure. LayoutParser also supports high level customization via e\u000ecient\n",
|
||
"layout annotation and model training functions. These improve model accuracy\n",
|
||
"on the target samples. The community platform enables the easy sharing of DIA\n",
|
||
"models and whole digitization pipelines to promote reusability and reproducibility.\n",
|
||
"A collection of detailed documentation, tutorials and exemplar projects make\n",
|
||
"LayoutParser easy to learn and use.\n",
|
||
"AllenNLP [ 8] and transformers [ 34] have provided the community with complete\n",
|
||
"DL-based support for developing and deploying models for general computer\n",
|
||
"vision and natural language processing problems. LayoutParser , on the other\n",
|
||
"hand, specializes speci\f",
|
||
"\n",
|
||
"cally in DIA tasks. LayoutParser is also equipped with a\n",
|
||
"community platform inspired by established model hubs such as Torch Hub [23]\n",
|
||
"andTensorFlow Hub [1]. It enables the sharing of pretrained models as well as\n",
|
||
"full document processing pipelines that are unique to DIA tasks.\n",
|
||
"There have been a variety of document data collections to facilitate the\n",
|
||
"development of DL models. Some examples include PRImA [ 3](magazine layouts),\n",
|
||
"PubLayNet [ 38](academic paper layouts), Table Bank [ 18](tables in academic\n",
|
||
"papers), Newspaper Navigator Dataset [ 16,17](newspaper \f",
|
||
"\n",
|
||
"gure layouts) and\n",
|
||
"HJDataset [31](historical Japanese document layouts). A spectrum of models\n",
|
||
"trained on these datasets are currently available in the LayoutParser model zoo\n",
|
||
"to support di\u000b",
|
||
"\n",
|
||
"erent use cases.\n",
|
||
"3 The Core LayoutParser Library\n",
|
||
"At the core of LayoutParser is an o\u000b",
|
||
"\n",
|
||
"-the-shelf toolkit that streamlines DL-\n",
|
||
"based document image analysis. Five components support a simple interface\n",
|
||
"with comprehensive functionalities: 1) The layout detection models enable using\n",
|
||
"pre-trained or self-trained DL models for layout detection with just four lines\n",
|
||
"of code. 2) The detected layout information is stored in carefully engineered\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from langchain.vectorstores import FAISS\n",
|
||
"from langchain.embeddings.openai import OpenAIEmbeddings\n",
|
||
"\n",
|
||
"faiss_index = FAISS.from_documents(pages, OpenAIEmbeddings())\n",
|
||
"docs = faiss_index.similarity_search(\"How will the community be engaged?\", k=2)\n",
|
||
"for doc in docs:\n",
|
||
" print(str(doc.metadata[\"page\"]) + \":\", doc.page_content)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "6d5c9879",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Using MathPix\n",
|
||
"\n",
|
||
"Inspired by Daniel Gross's [https://gist.github.com/danielgross/3ab4104e14faccc12b49200843adab21](https://gist.github.com/danielgross/3ab4104e14faccc12b49200843adab21)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 1,
|
||
"id": "950eb58f",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from langchain.document_loaders import MathpixPDFLoader"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "cb6fd473",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"loader = MathpixPDFLoader(\"example_data/layout-parser-paper.pdf\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "a1d41e1a",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"data = loader.load()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "09d64998",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Using Unstructured"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"id": "0cc0cd42",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from langchain.document_loaders import UnstructuredPDFLoader"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"id": "082d557c",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"loader = UnstructuredPDFLoader(\"example_data/layout-parser-paper.pdf\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "df11c953",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"data = loader.load()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "09957371",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Retain Elements\n",
|
||
"\n",
|
||
"Under the hood, Unstructured creates different \"elements\" for different chunks of text. By default we combine those together, but you can easily keep that separation by specifying `mode=\"elements\"`."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "0fab833b",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"loader = UnstructuredPDFLoader(\"example_data/layout-parser-paper.pdf\", mode=\"elements\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "c3e8ff1b",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"data = loader.load()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"id": "43c23d2d",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Document(page_content='LayoutParser: A Unified Toolkit for Deep\\nLearning Based Document Image Analysis\\nZejiang Shen1 (<28>), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain\\nLee4, Jacob Carlson3, and Weining Li5\\n1 Allen Institute for AI\\nshannons@allenai.org\\n2 Brown University\\nruochen zhang@brown.edu\\n3 Harvard University\\n{melissadell,jacob carlson}@fas.harvard.edu\\n4 University of Washington\\nbcgl@cs.washington.edu\\n5 University of Waterloo\\nw422li@uwaterloo.ca\\nAbstract. Recent advances in document image analysis (DIA) have been\\nprimarily driven by the application of neural networks. Ideally, research\\noutcomes could be easily deployed in production and extended for further\\ninvestigation. However, various factors like loosely organized codebases\\nand sophisticated model configurations complicate the easy reuse of im-\\nportant innovations by a wide audience. Though there have been on-going\\nefforts to improve reusability and simplify deep learning (DL) model\\ndevelopment in disciplines like natural language processing and computer\\nvision, none of them are optimized for challenges in the domain of DIA.\\nThis represents a major gap in the existing toolkit, as DIA is central to\\nacademic research across a wide range of disciplines in the social sciences\\nand humanities. This paper introduces LayoutParser, an open-source\\nlibrary for streamlining the usage of DL in DIA research and applica-\\ntions. The core LayoutParser library comes with a set of simple and\\nintuitive interfaces for applying and customizing DL models for layout de-\\ntection, character recognition, and many other document processing tasks.\\nTo promote extensibility, LayoutParser also incorporates a community\\nplatform for sharing both pre-trained models and full document digiti-\\nzation pipelines. We demonstrate that LayoutParser is helpful for both\\nlightweight and large-scale digitization pipelines in real-word use cases.\\nThe library is publicly available at https://layout-parser.github.io.\\nKeywords: Document Image Analysis · Deep Learning · Layout Analysis\\n· Character Recognition · Open Source library · Toolkit.\\n1\\nIntroduction\\nDeep Learning(DL)-based approaches are the state-of-the-art for a wide range of\\ndocument image analysis (DIA) tasks including document image classification [11,\\narXiv:2103.15348v2 [cs.CV] 21 Jun 2021\\n', 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)"
|
||
]
|
||
},
|
||
"execution_count": 5,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"data[0]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "278c881f",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Fetching remote PDFs using Unstructured\n",
|
||
"\n",
|
||
"This covers how to load online pdfs into a document format that we can use downstream. This can be used for various online pdf sites such as https://open.umn.edu/opentextbooks/textbooks/ and https://arxiv.org/archive/\n",
|
||
"\n",
|
||
"Note: all other pdf loaders can also be used to fetch remote PDFs, but `OnlinePDFLoader` is a legacy function, and works specifically with `UnstructuredPDFLoader`.\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "0c2686fc",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from langchain.document_loaders import OnlinePDFLoader"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "101e0b82",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"loader = OnlinePDFLoader(\"https://arxiv.org/pdf/2302.03803.pdf\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "be3ccbfa",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"data = loader.load()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "e1298dd6",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[Document(page_content='A WEAK ( k, k ) -LEFSCHETZ THEOREM FOR PROJECTIVE TORIC ORBIFOLDS\\n\\nWilliam D. Montoya\\n\\nInstituto de Matem´atica, Estat´ıstica e Computa¸c˜ao Cient´ıfica,\\n\\nIn [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\\n\\nKeywords: (1,1)- Lefschetz theorem, Hodge conjecture, toric varieties, complete intersection Email: wmontoya@ime.unicamp.br\\n\\ntheorem ([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\\n\\nAcknowledgement. I thank Prof. Ugo Bruzzo and Tiago Fonseca for useful discus- sions. I also acknowledge support from FAPESP postdoctoral grant No. 2019/23499-7.\\n\\nLet M be a free abelian group of rank d , let N = Hom ( M, Z ) , and N R = N ⊗ Z R .\\n\\nif there exist k linearly independent primitive elements e\\n\\n, . . . , e k ∈ N such that σ = { µ\\n\\ne\\n\\n+ ⋯ + 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\\n\\n, . . . , σ t } of rational simplicial cones is called a rational simplicial complete d -dimensional fan if:\\n\\nall faces of cones in Σ are in Σ ;\\n\\nif σ, σ ′ ∈ Σ then σ ∩ σ ′ < σ and σ ∩ σ ′ < σ ′ ;\\n\\nN R = σ\\n\\n∪ ⋅ ⋅ ⋅ ∪ σ t .\\n\\nA 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\\n\\nFor a cone σ ∈ Σ, ˆ σ is the set of 1-dimensional cone in Σ that are not contained in σ\\n\\nand x ˆ σ ∶ = ∏ ρ ∈ ˆ σ x ρ is the associated monomial in S .\\n\\nDefinition 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.\\n\\nProposition 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 .\\n\\nNow 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.\\n\\nDefinition 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 ) .\\n\\nDefinition 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\\n\\nRoughly speaking the local geometry of orbifolds reduces to local G -invariant geometry.\\n\\nWe 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:\\n\\n(1,1)-Lefschetz theorem for projective toric orbifolds\\n\\nDefinition 3.1. A subvariety X ⊂ P d Σ is quasi-smooth if V ( I X ) ⊂ A #Σ ( 1 ) is smooth outside\\n\\nExample 3.2 . Quasi-smooth hypersurfaces or more generally quasi-smooth intersection sub-\\n\\nExample 3.2 . Quasi-smooth hypersurfaces or more generally quasi-smooth intersection sub- varieties are quasi-smooth subvarieties (see [2] or [7] for more details).\\n\\nRemark 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\\n\\nProof. From the exponential short exact sequence\\n\\nwe have a long exact sequence in cohomology\\n\\nH 1 (O ∗ X ) → H 2 ( X, Z ) → H 2 (O X ) ≃ H 0 , 2 ( X )\\n\\nwhere the last isomorphisms is due to Steenbrink in [9]. Now, it is enough to prove the commutativity of the next diagram\\n\\nwhere the last isomorphisms is due to Steenbrink in [9]. Now,\\n\\nH 2 ( X, Z ) / / H 2 ( X, O X ) ≃ Dolbeault H 2 ( X, C ) deRham ≃ H 2 dR ( X, C ) / / H 0 , 2 ¯ ∂ ( X )\\n\\nof the proof follows as the ( 1 , 1 ) -Lefschetz theorem in [6].\\n\\nRemark 3.5 . For k = 1 and P d Σ as the projective space, we recover the classical ( 1 , 1 ) - Lefschetz theorem.\\n\\nBy the Hard Lefschetz Theorem for projective orbifolds (see [11] for details) we\\n\\nBy the Hard Lefschetz Theorem for projective orbifolds (see [11] for details) we get an isomorphism of cohomologies :\\n\\ngiven by the Lefschetz morphism and since it is a morphism of Hodge structures, we have:\\n\\nH 1 , 1 ( X, Q ) ≃ H dim X − 1 , dim X − 1 ( X, Q )\\n\\nCorollary 3.6. If the dimension of X is 1 , 2 or 3 . The Hodge conjecture holds on X\\n\\nProof. 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.\\n\\nCayley trick and Cayley proposition\\n\\nThe 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\\n\\nMoreover 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].\\n\\nWe will denote P ( E ) as P d + s − 1 Σ ,X to keep track of its relation with X and P d Σ .\\n\\nThe following is a key remark.\\n\\nRemark 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 } .\\n\\nFor 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].\\n\\nDefinition 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.\\n\\nH 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.\\n\\nThe next Proposition is the Cayley proposition.\\n\\nProposition 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\\n\\nRemark 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.\\n\\nTheorem 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.\\n\\nthe Hodge conjecture holds.\\n\\nProof. 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\\n\\ntoric 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\\n\\nfurthermore it has codimension k .\\n\\nClaim: { 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 ] .\\n\\nRemark 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:\\n\\nargument we have:\\n\\nProposition 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 .\\n\\nCorollary 5.4. If the dimension of Y is 2 s − 1 , 2 s or 2 s + 1 then the Hodge conjecture holds on Y .\\n\\nProof. By Proposition 5.3 and Corollary 3.6.\\n\\n[\\n\\n] Angella, D. Cohomologies of certain orbifolds. Journal of Geometry and Physics\\n\\n(\\n\\n),\\n\\n–\\n\\n[\\n\\n] Batyrev, V. V., and Cox, D. A. On the Hodge structure of projective hypersur- faces in toric varieties. Duke Mathematical Journal\\n\\n,\\n\\n(Aug\\n\\n). [\\n\\n] 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 (\\n\\n). [\\n\\n] Caramello Jr, F. C. Introduction to orbifolds. a\\n\\niv:\\n\\nv\\n\\n(\\n\\n). [\\n\\n] Cox, D., Little, J., and Schenck, H. Toric varieties, vol.\\n\\nAmerican Math- ematical Soc.,\\n\\n[\\n\\n] Griffiths, P., and Harris, J. Principles of Algebraic Geometry. John Wiley & Sons, Ltd,\\n\\n[\\n\\n] Mavlyutov, A. R. Cohomology of complete intersections in toric varieties. Pub- lished in Pacific J. of Math.\\n\\nNo.\\n\\n(\\n\\n),\\n\\n–\\n\\n[\\n\\n] Satake, I. On a Generalization of the Notion of Manifold. Proceedings of the National Academy of Sciences of the United States of America\\n\\n,\\n\\n(\\n\\n),\\n\\n–\\n\\n[\\n\\n] Steenbrink, J. H. M. Intersection form for quasi-homogeneous singularities. Com- positio Mathematica\\n\\n,\\n\\n(\\n\\n),\\n\\n–\\n\\n[\\n\\n] Voisin, C. Hodge Theory and Complex Algebraic Geometry I, vol.\\n\\nof Cambridge Studies in Advanced Mathematics . Cambridge University Press,\\n\\n[\\n\\n] Wang, Z. Z., and Zaffran, D. A remark on the Hard Lefschetz theorem for K¨ahler orbifolds. Proceedings of the American Mathematical Society\\n\\n,\\n\\n(Aug\\n\\n).\\n\\n[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).\\n\\n[\\n\\n] 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 (\\n\\n).\\n\\n[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).\\n\\nA. 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)]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(data)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "05187b33",
|
||
"metadata": {},
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "21998d18",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Using PDFMiner"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "2f0cc9ff",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from langchain.document_loaders import PDFMinerLoader"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "42b531e8",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"loader = PDFMinerLoader(\"example_data/layout-parser-paper.pdf\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "010d5cdd",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"data = loader.load()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "c90a5fe8",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Using PDFMiner to generate HTML text"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "eb785e1c",
|
||
"metadata": {},
|
||
"source": [
|
||
"This can be helpful for chunking texts semantically into sections as the output html content can be parsed via `BeautifulSoup` to get more structured and rich information about font size, page numbers, pdf headers/footers, etc."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 1,
|
||
"id": "601000d7",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from langchain.document_loaders import PDFMinerPDFasHTMLLoader"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 2,
|
||
"id": "a5525fb0",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"loader = PDFMinerPDFasHTMLLoader(\"example_data/layout-parser-paper.pdf\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"id": "dac7ff68",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"data = loader.load()[0] # entire pdf is loaded as a single Document"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"id": "0ba9f645",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from bs4 import BeautifulSoup\n",
|
||
"soup = BeautifulSoup(data.page_content,'html.parser')\n",
|
||
"content = soup.find_all('div')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"id": "35304e21",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import re\n",
|
||
"cur_fs = None\n",
|
||
"cur_text = ''\n",
|
||
"snippets = [] # first collect all snippets that have the same font size\n",
|
||
"for c in content:\n",
|
||
" sp = c.find('span')\n",
|
||
" if not sp:\n",
|
||
" continue\n",
|
||
" st = sp.get('style')\n",
|
||
" if not st:\n",
|
||
" continue\n",
|
||
" fs = re.findall('font-size:(\\d+)px',st)\n",
|
||
" if not fs:\n",
|
||
" continue\n",
|
||
" fs = int(fs[0])\n",
|
||
" if not cur_fs:\n",
|
||
" cur_fs = fs\n",
|
||
" if fs == cur_fs:\n",
|
||
" cur_text += c.text\n",
|
||
" else:\n",
|
||
" snippets.append((cur_text,cur_fs))\n",
|
||
" cur_fs = fs\n",
|
||
" cur_text = c.text\n",
|
||
"snippets.append((cur_text,cur_fs))\n",
|
||
"# Note: The above logic is very straightforward. One can also add more strategies such as removing duplicate snippets (as\n",
|
||
"# headers/footers in a PDF appear on multiple pages so if we find duplicatess safe to assume that it is redundant info)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "af8adf2f",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from langchain.docstore.document import Document\n",
|
||
"cur_idx = -1\n",
|
||
"semantic_snippets = []\n",
|
||
"# Assumption: headings have higher font size than their respective content\n",
|
||
"for s in snippets:\n",
|
||
" # if current snippet's font size > previous section's heading => it is a new heading\n",
|
||
" if not semantic_snippets or s[1] > semantic_snippets[cur_idx].metadata['heading_font']:\n",
|
||
" metadata={'heading':s[0], 'content_font': 0, 'heading_font': s[1]}\n",
|
||
" metadata.update(data.metadata)\n",
|
||
" semantic_snippets.append(Document(page_content='',metadata=metadata))\n",
|
||
" cur_idx += 1\n",
|
||
" continue\n",
|
||
" \n",
|
||
" # if current snippet's font size <= previous section's content => content belongs to the same section (one can also create\n",
|
||
" # a tree like structure for sub sections if needed but that may require some more thinking and may be data specific)\n",
|
||
" if not semantic_snippets[cur_idx].metadata['content_font'] or s[1] <= semantic_snippets[cur_idx].metadata['content_font']:\n",
|
||
" semantic_snippets[cur_idx].page_content += s[0]\n",
|
||
" semantic_snippets[cur_idx].metadata['content_font'] = max(s[1], semantic_snippets[cur_idx].metadata['content_font'])\n",
|
||
" continue\n",
|
||
" \n",
|
||
" # if current snippet's font size > previous section's content but less tha previous section's heading than also make a new \n",
|
||
" # section (e.g. title of a pdf will have the highest font size but we don't want it to subsume all sections)\n",
|
||
" metadata={'heading':s[0], 'content_font': 0, 'heading_font': s[1]}\n",
|
||
" metadata.update(data.metadata)\n",
|
||
" semantic_snippets.append(Document(page_content='',metadata=metadata))\n",
|
||
" cur_idx += 1"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "db7f6674",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Document(page_content='Recently, various DL models and datasets have been developed for layout analysis\\ntasks. The dhSegment [22] utilizes fully convolutional networks [20] for segmen-\\ntation tasks on historical documents. Object detection-based methods like Faster\\nR-CNN [28] and Mask R-CNN [12] are used for identifying document elements [38]\\nand detecting tables [30, 26]. Most recently, Graph Neural Networks [29] have also\\nbeen used in table detection [27]. However, these models are usually implemented\\nindividually and there is no unified framework to load and use such models.\\nThere has been a surge of interest in creating open-source tools for document\\nimage processing: a search of document image analysis in Github leads to 5M\\nrelevant code pieces 6; yet most of them rely on traditional rule-based methods\\nor provide limited functionalities. The closest prior research to our work is the\\nOCR-D project7, which also tries to build a complete toolkit for DIA. However,\\nsimilar to the platform developed by Neudecker et al. [21], it is designed for\\nanalyzing historical documents, and provides no supports for recent DL models.\\nThe DocumentLayoutAnalysis project8 focuses on processing born-digital PDF\\ndocuments via analyzing the stored PDF data. Repositories like DeepLayout9\\nand Detectron2-PubLayNet10 are individual deep learning models trained on\\nlayout analysis datasets without support for the full DIA pipeline. The Document\\nAnalysis and Exploitation (DAE) platform [15] and the DeepDIVA project [2]\\naim to improve the reproducibility of DIA methods (or DL models), yet they\\nare not actively maintained. OCR engines like Tesseract [14], easyOCR11 and\\npaddleOCR12 usually do not come with comprehensive functionalities for other\\nDIA tasks like layout analysis.\\nRecent years have also seen numerous efforts to create libraries for promoting\\nreproducibility and reusability in the field of DL. Libraries like Dectectron2 [35],\\n6 The number shown is obtained by specifying the search type as ‘code’.\\n7 https://ocr-d.de/en/about\\n8 https://github.com/BobLd/DocumentLayoutAnalysis\\n9 https://github.com/leonlulu/DeepLayout\\n10 https://github.com/hpanwar08/detectron2\\n11 https://github.com/JaidedAI/EasyOCR\\n12 https://github.com/PaddlePaddle/PaddleOCR\\n4\\nZ. Shen et al.\\nFig. 1: The overall architecture of LayoutParser. For an input document image,\\nthe core LayoutParser library provides a set of off-the-shelf tools for layout\\ndetection, OCR, visualization, and storage, backed by a carefully designed layout\\ndata structure. LayoutParser also supports high level customization via efficient\\nlayout annotation and model training functions. These improve model accuracy\\non the target samples. The community platform enables the easy sharing of DIA\\nmodels and whole digitization pipelines to promote reusability and reproducibility.\\nA collection of detailed documentation, tutorials and exemplar projects make\\nLayoutParser easy to learn and use.\\nAllenNLP [8] and transformers [34] have provided the community with complete\\nDL-based support for developing and deploying models for general computer\\nvision and natural language processing problems. LayoutParser, on the other\\nhand, specializes specifically in DIA tasks. LayoutParser is also equipped with a\\ncommunity platform inspired by established model hubs such as Torch Hub [23]\\nand TensorFlow Hub [1]. It enables the sharing of pretrained models as well as\\nfull document processing pipelines that are unique to DIA tasks.\\nThere have been a variety of document data collections to facilitate the\\ndevelopment of DL models. Some examples include PRImA [3](magazine layouts),\\nPubLayNet [38](academic paper layouts), Table Bank [18](tables in academic\\npapers), Newspaper Navigator Dataset [16, 17](newspaper figure layouts) and\\nHJDataset [31](historical Japanese document layouts). A spectrum of models\\ntrained on these datasets are currently available in the LayoutParser model zoo\\nto support different use cases.\\n', metadata={'heading': '2 Related Work\\n', 'content_font': 9, 'heading_font': 11, 'source': 'example_data/layout-parser-paper.pdf'})"
|
||
]
|
||
},
|
||
"execution_count": 7,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"semantic_snippets[4]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "cc2c2f4f",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Using PyMuPDF\n",
|
||
"\n",
|
||
"This is the fastest of the PDF parsing options, and contains detailed metadata about the PDF and its pages, as well as returns one document per page."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 1,
|
||
"id": "55f0c4d8",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from langchain.document_loaders import PyMuPDFLoader"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 2,
|
||
"id": "718cbfbc",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"loader = PyMuPDFLoader(\"example_data/layout-parser-paper.pdf\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"id": "f2f93a15",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"data = loader.load()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"id": "a24dfaa6",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Document(page_content='LayoutParser: A Unified Toolkit for Deep\\nLearning Based Document Image Analysis\\nZejiang Shen1 (<28>), Ruochen Zhang2, Melissa Dell3, Benjamin Charles Germain\\nLee4, Jacob Carlson3, and Weining Li5\\n1 Allen Institute for AI\\nshannons@allenai.org\\n2 Brown University\\nruochen zhang@brown.edu\\n3 Harvard University\\n{melissadell,jacob carlson}@fas.harvard.edu\\n4 University of Washington\\nbcgl@cs.washington.edu\\n5 University of Waterloo\\nw422li@uwaterloo.ca\\nAbstract. Recent advances in document image analysis (DIA) have been\\nprimarily driven by the application of neural networks. Ideally, research\\noutcomes could be easily deployed in production and extended for further\\ninvestigation. However, various factors like loosely organized codebases\\nand sophisticated model configurations complicate the easy reuse of im-\\nportant innovations by a wide audience. Though there have been on-going\\nefforts to improve reusability and simplify deep learning (DL) model\\ndevelopment in disciplines like natural language processing and computer\\nvision, none of them are optimized for challenges in the domain of DIA.\\nThis represents a major gap in the existing toolkit, as DIA is central to\\nacademic research across a wide range of disciplines in the social sciences\\nand humanities. This paper introduces LayoutParser, an open-source\\nlibrary for streamlining the usage of DL in DIA research and applica-\\ntions. The core LayoutParser library comes with a set of simple and\\nintuitive interfaces for applying and customizing DL models for layout de-\\ntection, character recognition, and many other document processing tasks.\\nTo promote extensibility, LayoutParser also incorporates a community\\nplatform for sharing both pre-trained models and full document digiti-\\nzation pipelines. We demonstrate that LayoutParser is helpful for both\\nlightweight and large-scale digitization pipelines in real-word use cases.\\nThe library is publicly available at https://layout-parser.github.io.\\nKeywords: Document Image Analysis · Deep Learning · Layout Analysis\\n· Character Recognition · Open Source library · Toolkit.\\n1\\nIntroduction\\nDeep Learning(DL)-based approaches are the state-of-the-art for a wide range of\\ndocument image analysis (DIA) tasks including document image classification [11,\\narXiv:2103.15348v2 [cs.CV] 21 Jun 2021\\n', 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)"
|
||
]
|
||
},
|
||
"execution_count": 4,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"data[0]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "83cb52a0",
|
||
"metadata": {},
|
||
"source": [
|
||
"Additionally, you can pass along any of the options from the [PyMuPDF documentation](https://pymupdf.readthedocs.io/en/latest/app1.html#plain-text/) as keyword arguments in the `load` call, and it will be pass along to the `get_text()` call."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "15b57eab",
|
||
"metadata": {},
|
||
"source": [
|
||
"## PyPDF Directory\n",
|
||
"\n",
|
||
"Load PDFs from directory"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 1,
|
||
"id": "b9e521d9",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from langchain.document_loaders import PyPDFDirectoryLoader"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 2,
|
||
"id": "4b20590f",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"loader = PyPDFDirectoryLoader(\"example_data/\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"id": "e5ead943",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"docs = loader.load()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "ea25b03c",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3 (ipykernel)",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.9.1"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|