跳至主要內容

圖片 (Images)

此章節介紹如何將圖片載入為文件格式，以便我們在下游與其他 LangChain 模組一起使用。

它使用 Unstructured 來處理各種圖片格式，例如 .jpg 和 .png。有關在本地設定 Unstructured 的更多說明（包括設定所需的系統依賴項），請參閱本指南。

使用 Unstructured​

%pip install --upgrade --quiet "unstructured[all-docs]"
from langchain_community.document_loaders.image import UnstructuredImageLoader

loader = UnstructuredImageLoader("./example_data/layout-parser-paper-screenshot.png")

data = loader.load()

data[0]
Document(page_content='2021\n\n2103.15348v2 [cs.CV] 21 Jun\n\narXiv\n\nLayoutParser: A Unified Toolkit for Deep Learning Based Document Image Analysis\n\nZejiang Shen! (&4), Ruochen Zhang?, Melissa Dell?, Benjamin Charles Germain Lee*, Jacob Carlson?, and Weining Li?\n\n1\n\nAllen Institute for AI shannons@allenai.org ? Brown University ruochen_zhang@brown. edu 3 Harvard University {melissadell, jacob_carlson}@fas.harvard.edu 4 University of Washington begl@cs.washington.edu 5 University of Waterloo w4221i@uwaterloo.ca\n\nAbstract. 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.\n\nKeywords: Document Image Analysis - Deep Learning - Layout Analysis - Character Recognition - Open Source library - Toolkit.\n\n1 Introduction\n\nDeep 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,', metadata={'source': './example_data/layout-parser-paper-screenshot.png'})

保留元素​

在底層，Unstructured 會為不同的文本區塊建立不同的「元素」。預設情況下，我們會將它們組合在一起，但您可以通過指定 mode="elements" 來保留這種分離。

loader = UnstructuredImageLoader(
    "./example_data/layout-parser-paper-screenshot.png", mode="elements"
)

data = loader.load()

data[0]
Document(page_content='2021', metadata={'source': './example_data/layout-parser-paper-screenshot.png', 'coordinates': {'points': ((47.0, 492.0), (47.0, 591.0), (83.0, 591.0), (83.0, 492.0)), 'system': 'PixelSpace', 'layout_width': 1624, 'layout_height': 1920}, 'last_modified': '2024-07-01T10:38:29', 'filetype': 'PNG', 'languages': ['eng'], 'page_number': 1, 'file_directory': './example_data', 'filename': 'layout-parser-paper-screenshot.png', 'category': 'UncategorizedText'})

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