A PDF parser or PDF scraper is software that you can use to extract data from PDF documents. The PDF parser can be provided in the form of a library for developers or as a stand-alone software product for end-users.
The PDF parser is primarily used to extract data from batches of PDF files. Manual data entry like copy and paste is a common alternative when you only need to extract data from a small number of documents.
PDF files are the first choice for a wide variety of documents, from books, presentations, reports, pamphlets to invoices and orders. While extracting PDF files, you may think of topics like, ‘How to parse PDF in python?’, or ‘How to parse PDF in Java?’. There’s a one-stop solution to all your queries!
KlearStack is an AI-based data extraction software that offers an easy-to-use user interface and API. The software offers a variety of features for their users and also offers a fully functional free trial.
Although PDF can embed rich media types and attachments, PDF parser or PDF parsing solutions are typically used to extract:
Table of Contents
● Data fields
If the PDF is created from a dataset or contains fields having individual data, the PDF parser can extract it for you. You can organize the data in a particular schema according to the field so you can copy it elsewhere.
● Images
If the PDF document contains images, the PDF parser can extract and save the individual images. This is especially useful if you want to recreate images elsewhere from these documents. This saves you the trouble of taking multiple low-quality screenshots.
● Text paragraphs
This is the simplest form of data. If the PDF document contains text, you can copy and paste it, but it does not resolve the word processor formatting issue. The PDF parser extracts the text in the correct format and makes it ready to use.
● Tables
Modern PDF parsers like KlearStack can detect the presence of tables in a document. This is a significant advantage, as most older PDF parsers display all kinds of data as paragraphs, ruin the table, and eventually require the user to manually select and copy the data.
Developer-friendly command-line PDF parsing tools can get the following properties that primarily describe the physical structure of a PDF document:
- Objects
- Headers
- Trailers
- Metadata
- Cross-referenced tables
Use cases of PDF parser
PDF parsers or PDF scrapers are often used in use cases dealing with intelligent document processing or business process automation. This is intended for all document management workflows in enterprises that need to automatically extract data from PDF documents.
- Invoice automation Intelligently extracts data from invoices.
- Receipt Scanner or Receipt OCR Extract meaningful data in real-time from items such as receipts, invoices, orders, expense receipts, work orders, invoices, checks, and more.
- ID Card Verification Scan your ID card to extract your name, address, date of birth, and other details.
Other Common Uses for Digitizing Documents:
- Extracting Tables Gets relevant information from the table structure of any document.
- Companies in the finance, construction, health, insurance, banking, hospitality, and automotive industries use PDF parsers such as KlearStack to analyze or scrape PDFs of important data.
Benefits of PDF parsing
Analyzing PDF documents used in your company’s workflow can greatly streamline your business processes. Automated PDF parsing software like KlearStack can further streamline business processes by using automation, AI, and ML capabilities to significantly reduce inefficiencies. Here are some of the benefits of PDF parsing:
- Save time and money
- Reduce reliance on manual processes and data entry
- Eliminate errors, duplicates and redo
- Improve accuracy and scope
- Automated workflow Optimize the data exchange
- Eliminate the use and storage of physical documents
- Convert unstructured data to structured formats such as XML, JSON, Excel, CSV
Companies and organizations from different sectors like economic, banking, and more use KlearStack for easy and simple data extraction.
How to parse PDF files in Python/Java?
Most text analysis libraries or frameworks were developed only in Python. This allows you to take advantage of text analysis, once you have extracted useful information from a PDF, you can easily process that data into a machine learning or natural language model.
To parse PDF files in Python, there are various tools available on the web like PyPDF2, PDFMiner, which allow PDF parsing in Python.
It is very simple to read and parse PDFs to JSON format. Users can simply use various tools like Pdf parser, KlearStack, etc. to implement the parsing process to their PDF files. Users can also do this the long way through coding. However, KlearStack has made it extremely easy to parse PDF files in various formats without spending too much time. It is highly accurate and time efficient.
How to parse PDF files using KlearStack?
The KlearStack PDF parser has pre-trained models for certain types of documents such as invoices, receipts, passports, insurance policies, purchase orders, NACH mandates etc. Simply visit the official website of KlearStack and opt for a free trial offered by the company for a limited time period. You may also buy the premium membership if you want to try the special features offered by the company.
Why is KlearStack the best PDF parsing solution?
KlearStack is an accurate and strong PDF parser that is easy to set up and use, providing a working pre-trained model for common enterprise use cases. Analyze the PDF in seconds, or train your model to analyze the PDF data on a large scale. The advantages of using KlearStack over other PDF parsers go far beyond the improvement of inaccuracy.
- KlearStack provides end-to-end data extraction (including line items) from invoices with very high accuracy.
- Where most options do not allow you to accurately extract your bid information, KlearStack is a highly accurate and safe PDF parser.
- This is template-independent data extraction software. No template coordinates or rules are needed!
- Adaptive deep learning models are increasingly training themselves, thereby improving accuracy while PDF parsing.
- Unlike other alternatives on the market, this is more than just a data extracting solution.
- Free trial for a limited time.
Quick and efficient Data extraction: The KlearStack way!
KlearStack‘s AI-based data extracting software is seen as the future of data extraction. Our software not only scans and extracts image data but also understands the image data and ultimately provides relevant error-free digital text.
In addition, the biggest advantage of this data extracting software is that images and files do not have to be in any format or template. Therefore, you can enjoy seamless data extraction without worrying about errors.
Artificial intelligence and adaptive machine learning algorithms allow KlearStack’s PDF parsing software to extract information from images such as application forms and license plates. It helps you to review and organize this data efficiently.
This software supports large-scale automation of image data extraction. This can be useful for banks, healthcare, retail, and many other economic sectors. Users can easily parse or scrape any PDF to their preferred format within minutes!
