The development of EDI and APIs greatly supports us in integrating data. However, there’s still an extremely huge amount of unstructured data in documents, spreadsheets, images, emails, and paper sources that need systemizing.
It takes a lot of time and effort to read, organize, search, and share data manually while this precious time can be spent on business strategies and development. That’s where intelligent document parsing solutions come in into play to make data entry a thing of the past.
Why do you need a document parser? How does a document parser work? Which types of businesses have a demand for parsing files? What problems we’re facing when using these tools? Our today’s article will help answer all these questions in detail.
Before that, we’ll define what document parsing is together.
Without further ado, let’s get started!
Document parsing refers to the process of analyzing a document and extracting information from it, either in a structured or unstructured format. This activity is suitable for extracting resumes, invoices, orders, reports, and other scanned documents.
We can parse information from various document types, from Word and PDF to images. You can even choose specific information in a document to extract. For instance, text paragraphs, data fields (dates, address, etc.), table and list format, and images.
To set up a document parser, you’ll first need to determine your desired file format. Due to a great number of document layouts, you’re required to choose the tool that is compatible with them.
It’s necessary to configure the document parser. This can be done by specifying the fields you want to extract and the rules for extracting them.
The document parser will then read through the document and extract the information you have specified. This information can serve various purposes, such as creating a database or generating reports.
Document parsers benefit both individuals and businesses in multiple ways. Besides data entry automation, you’re able to digitalize data as well as improve its reliability.
Manual data entry is time-consuming, without a doubt. You have to screen document by document, then re-enter the information into a system. Even with the help of scanning tools, they just assist in storing data. Imagine you have hundreds of files to parse, it would be a nightmare.
By making use of intelligent document parsers, not only can you remove the physical storage of hard copies but also speed up the process incredibly. Rather than wasting 10 to 15 minutes on a file, it just takes a few seconds to complete the task.
Paperwork takes up a lot of space. A good example of this is HR files. HR staff has to organize and maintain employee records, training documents, health insurance, payroll records, and tons more. The quality of these files will grow dramatically over time.
Thanks to digital parsing software, we’re able to replace paper copies of data. Everything will be kept track in only one system, making searching for and using information much easier.
Entering data manually is prone to human error, especially when you have tons of files to handle. Fortunately, document parser tools can help eliminate this labor work which leads to an improvement in data accuracy.
We can extract information using the optical character recognition (OCR) method. For a better explanation, it uses rule-based and machine learning-based approaches to examine the data and extract information from imported documents.
This method best suits well-structured files like resumes, loan applications, and invoices. It requires you to create a template of the document as a reference to apply rules on certain data positions.
Bear in mind that this way requires you to import the same document format as the pre-defined template. Even if there are just tiny differences in the file, it will fail to apply the rule-based approach.
For unstructured data, it’s highly recommended to use model-based or learning-based approaches. You need to train a lot of documents via Machine Learning (ML) and Natural Language Processing (NLP) so that the model can improve the recognition and extraction capability.
Actually, we don’t separate rule-based or model-based approaches. Instead, we’ll combine them for better document parsing performance.
Data parsing technology serves various types of business documents, from files related to finance and accounting to HR papers.
Anytime your business makes a purchase of goods or services from different vendors, the accounting department must process them to check for problems, confirm details, then release payments. This process wastes accountants a lot of time, particularly in medium and large businesses.
It’s of significance to use machine learning to extract invoice files. You can parse multiple data points in invoices, including price, name, and quantity to structured data. This information then will be merged into downstream systems like ERPs and CRM as well as internal databases.
Your business gets purchase orders via email attachments or fax in various formats. You need a solution to automate the order fulfillment process.
Document parsers work as purchase order readers, enabling you to remove manual data entry and send order details to your order management system, accounting system, or any specific endpoint.
Businesses offering physical products have to deal with plenty of delivery reports, shipping notices, and bills. Document parser software gives you a helping hand in extracting vital data from these reports as easily as pie.
Every business involves in many legal documents, from rental and leasing contracts to warranty and insurance agreements. Standardizing these papers by hand would exhaust you, not to mention the error-prone issue.
Document parsers lay down the burden on your shoulder by processing new contracts and then returning better-structured data.
Talking about business documents, it would be a huge missing out without mentioning HR papers. Your HR department has to manage countless files, enrollment forms, resumes, reports, and payrolls, just to name a few.
Information extractors will come in handy to systemize these files, making it much easier for you to organize, find, and use the data. Take Dr.Parser as an example, it supports parsing information in CVs so HR no longer has to screen resumes manually.
There still exist several challenges in document parsing technology that you should keep in mind.
In fact, it’s impossible to ensure data extraction is 100% correct. As we already discussed, your data can be shown in numerous formats. This prevents training models from performing consistently.
Information in resumes is a case in point. Company names and job titles will be presented in different ways, depending on the industry or area. Even date time also have at least 5 formats. That’s why you have to work out problems of both computer vision and natural language processing.
We can’t deny debugging in AI applications, particularly when building large networks. There are a lot of problems to solve but not all people can understand or handle them.
To be honest, only a few document parsers are available in multiple languages. It’s because each language requires good quality and quantity of training data.
Despite burning issues, document parsing still plays a key role in simplifying the paper workload of every business. Not only does it help digitalize data but it also improves reliability. It’s now applied in different business fields, from finance and accounting to human resources files and general business documents.
You can also refer to our Extract Text from PDF Resumes Using PyMuPDF and Python article to better understand text extraction.
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