Docparser for Data Providers: Extract PDF Data Fast
Scrape data from PDF documents on scale. Docparser offers a powerful set of tools to convert semi-structured PDF documents into easy-to-handle structured data.
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PDF Documents Hold Massive Amounts Of Data
In today’s work environment, PDF documents are the go-to solution for exchanging business data. A pixel perfect representation on all devices makes PDF a great replacement for “paper” and it is widely used to exchange business documents, such as Invoices, Purchase Orders, Reports, Work Orders, Price Lists & Product Catalogs, etc. – internally as well as between trading partners.
While PDF documents are easily readable by humans, only a small percentage of them come with machine readable meta data. Accessing the massive amounts of text data stored in PDF documents and converting it to easy-to-handle structured data is a non-trivial task. Unlike other document formats (e.g. XML, HTML), the PDF standard does not provide any hiercharchical tags, which would ease extracting, structuring and understanding the data programatically.
Scrape PDF Documents Like You Would Scrape The Web
When it comes to extracting data from PDF documents, manually re-keying is often the default solution. Manual data entry is however tedious, error-prone and costly. Luckily, there are better ways of extracting data from PDF documents.
Docparser is a PDF scraper software that allows you to automatically pull data from recurring PDF documents on scale. Like web-scraping (collecting data by crawling the internet), scraping PDF documents is a powerful method to automatically convert semi-structured text documents into structured data.
RefinePro helps organizations manage external data acquisition from sourcing and collecting third party data to loading them into their system. Our customers rely on RefinePro’s tool suite and processes to monitor prices from product catalogs or combine data released by governments or regulatory bodies. Unfortunately, those data are often locked in PDF files.
Our data ingestion workflow needs to be flexible to support the variety and the ever-changing format of data sources while lowering the effort to maintain our processes. Docparser is essential to balance both aspects. The Docparser API and webhooks allowed us to integrate the PDF extraction task directly in our workflow. When a file format changes, we use Docparser user interface to quickly and easily update a parser settings.
Martin – refinepro.com
The Docparser PDF Scraper Software
Docparser is a cloud PDF scraper software that provides flexible data extraction and conversion solutions for businesses worldwide. Whether you’re a corporation or a bootstrapped SaaS, Docparser comes with built-in OCR capabilities and offers ready-to-use templates for many use-cases. Setting up your first document parser takes usually less than 20 minutes and no programming is required.
Docparser allows you to extract data fields from fixed positions inside the document with a point and click interface. Extracting data from variable locations is possible thanks to smart filters and pattern matching algorithms. Table row parsing is a snap too, as you can define the column breaks and the overall area that the table resides.
How Do I Scrape Batch PDF Files?
Just sign up for a Docparser account, the first 100 scraped documents are free and the workflow is actually quite simple.
- Add a few batch documents. These will act as training data
- Train the system for each type of document you want to process by using our point and click system
- Set up an automated process to fetch documents, process them, and dispatch the data
Docparser offers a wide range on integration options. Documents can be manually uploaded, sent as email attachments, imported through one of our integration partner or with our REST HTTP API. Once the data got parsed from documents, it can be made available in various file formats (Excel, JSON, XML) or automatically sent to any private API or hundreds of software products in real time thanks to our Zapier and Workato integration.
Over a year ago we were looking for an OCR (Optical Character Recognition) solution for our business. We collect and publish public information and at the time we were entering about 1.3 million documents per year. While some of the documents were available in an electronic format that we could easily import into our system, the majority were paper based. We felt strongly that with the right solution we would be able to increase the accuracy and speed of the data entered into our system. Our hope was to find a system that would let us scale our business using existing resources.
We reviewed several OCR solutions. One in particular looked good and we had even moved to a design and implementation stage. The solution was expensive and the technology was so complicated that much of the cost would be tied up in the development of the OCR templates. This created a huge issue for us because of the variety of documents we collect. We have thousands and almost all are different. This was going to require a lot of custom development, which meant money, lot’s of money.
Then someone pointed me to Docparser. That changed everything. I was introduced to a system that was amazingly simple at a fraction of the price of every other system we had reviewed. Docparser was the perfect solution for us. It took less than an hour to evaluate and test the initial functionality and know that we had stumbled upon a powerful OCR system that would solve the pains we were having in our business. Moritz and his team have been great to work with even though their software is so powerful we seldom need to speak to them. But when we do they are quick to respond. We have been able to create hundreds of ‘parsers’ that take our document images and convert them to data that we map over to our database for automatic entry. The API provided by docparser has allowed us to create a seamless integration to our system that has helped increase our data entry efficency by over 35%. Their interface is simple enough that my own staff creates the powerful parsers that extract the data from the images we collect.
David Mineer – Construction Monitor
