If you are searching for a solution to scrape data from pdf, here is an article that can help you with the task.
With the ever-increasing data, data handling tools are evolving to match the changing demands of businesses. More and more companies are going digital, so they need to regularly handle structured and unstructured data available in different file formats such as .json, .txt,.csv, .jpg,.pdf, and so on. Manually extracting data from these formats is a tedious and time-consuming process. Hence, businesses prefer automating the extraction of data for the sake of speed and convenience. Automated data extraction from these formats is usually achieved by a process known as 'Scraping .' It is a process in which one program scrapes data from a website or an output of another program. There are several open-source or commercial tools available for scraping data from documents.
These days, PDFs (Portable Document Format) are considered a suitable and convenient digital alternative to traditional paper-based documents. One of the key benefits is their compatibility with the commonly used operating systems (Windows, Mac, or Linux). Besides, these are portable, smaller in size, easy to share or upload, and perfectly readable by humans. You'll likely find important business documents like contracts, bills, invoices, purchase or work orders, reports, etc., in PDF format as they can not be easily edited and can be conveniently printed or shared within/outside the organization. Since these are non-editable, the question arises whether it's possible to extract information from them for any data analysis. The answer is, of course, yes. These documents can be scraped to extract valuable information using specialized OCR-based tools.
Any business today requires a sufficient amount of data for analysis and making data-driven decisions. At times, this required data, such as customer market data, is scattered and in an unstructured format on the internet. The business needs a good understanding of its customers and the market to decide on relevant marketing and sales strategies. Thus, to perform periodic market research and data analysis, companies are required to scrape documents (invoices, bills, checks or expenses, etc.) to prepare data as per their machine learning requirements. All these above documents are normally stored as PDFs which require scraping to extract the relevant data.
Although structured data is preferred in machine learning, in the real world, most of the required data comes in an unstructured format. It is well-known that working with unstructured data is challenging and could waste valuable resources if performed incorrectly. E.g., banking and financial organizations or government agencies handle printed and electronic documents, i.e., PDFs that require proper conversion to formats such as tables before storage and further processing. Automating this data extraction task can make the process more reliable and expedite the workflow in an organization.
PDFs are generally used in educational institutes, government or non-profit as well as commercial organizations. This is due to the fact that PDFs are useful to create and securely share as eBooks, White Papers or research documents, digital documents such as invoices or contracts, etc. But capturing, extracting, and processing the embedded data in PDFs correctly is essential for the success of any data analysis. Tech companies are making this possible through efficient use of AI technologies such as NLP (Natural Language Processing), Computer Vision, Deep Learning, and Machine Learning, enabling machines to process and transform unstructured and semi-structured information into usable data. These document intelligence technologies are also called IDP (intelligent document processing). VisionERA is an IDP for text recognition that is capable of efficiently handling various types of documents quickly. You can find more details at the end of this article.
The tools to extract data from PDFs are commonly known as PDF scrapers. These tools provide an efficient, powerful, and scalable method for extracting massive volumes of data from PDFs and converting them to machine-readable structured data. Data extracted from PDFs may be easily handled in automated workflows, significantly improving organizational workflows. Commercial tools are generally used by companies in banking and finance, construction, healthcare, insurance, hospitality, and more to scrape PDFs for useful data.
Scraping pdf for extracting relevant data and storing it into a tabular format is essential to prepare the data for analysis. It is possible to use open-source tools in Python, such as PyPDF2, Tabula-py, Slate, Camelot, PDFQuery, etc., or commercial scraping applications to extract unstructured data. Since scraping PDFs could be challenging due to the changing layout and structure of documents, not every available tool can be reliable for this task. It is, therefore, crucial to select a proper tool depending on the project goals and the type of data to be extracted from the PDF. Hence, to extract unstructured data from PDF, a strong and accurate PDF scraper offering OCR, AI, and ML capabilities is preferred by businesses. Along with these, it is expected for the tool to be simple in implementation, require no expertise, and simultaneously provide multiple templates for reuse in common organizational use cases. The commercial solutions tend to be perfectly scalable as they can scrape PDFs in a few seconds and are adept at handling unstructured data, common data restrictions, multi-page documents, tables, and multi-line items.
PDF documents are increasingly used for important business documents such as invoices, questionnaires, contracts, etc. Unlike CSV or Excel files, the information in the PDFs is unstructured, which is tedious to manually extract and store for further processing. Open-source tools are likely to work for data extraction of simpler documents but might not be a good solution for complex, unstructured data occurring in different documents. Hence, scraping PDFs using commercial tools is a preferred solution for companies looking to reliably automate the process of data extraction from documents. Commercial tools, such as the VisionERA, can efficiently handle the changing formats of the form and still recognize the presence of text in the document or image.
About us: VisionERA is an Intelligent Document Processing (IDP) platform capable of handling various types of documents for text recognition and image processing. It has the capacity to extract and validate data for bulk volumes with minimal intervention. Also, the platform can be molded as per requirements for any industry and use case because of its custom DIY workflow feature. It is a scalable and flexible platform providing end-to-end document automation for any organization.
Are you looking for a document processing solution that uses the enhanced capabilities for text recognition in deep learning? Setup a demo today by clicking the CTA below or simply send us a query through the contact us page!