Automating data entry has become a necessity for an increasingly large number of organizations. The many hours that people spend typing information would be far more profitable if they spent it providing customer service, improving products, or marketing their business. So it’s no surprise that the demand for data extraction tools is continually growing.
While searching for the adequate solution for your data extraction needs, you’ll come across two main branches of document parsers: AI-trained and user-defined. Not sure what that means? Well, that’s what we’re going to discuss in this post. For now, just keep in mind that AI-trained parsers rely on AI models whereas user-defined parsers rely on rules set by users.
In this document parsing comparison, we’re going to explain the pros and cons of each type of document parser so that you can determine which one suits your needs best.
The main benefits of document parsers
Regardless of their type, document parsers are popular automation tools used by organizations (big and small alike) to streamline their processes and relieve their employees from the tedium of inputting data manually.
In case you need a refresher, the main benefits of using a document parser are:
- Time savings and enhanced productivity: through automation, employees can skip hours upon hours of manual document processing and spend more time on tasks that are more important. And in addition to saving time, this will help make data more accessible and searchable, thus boosting the productivity of anyone who needs that data to perform their role.
- Reduced costs: manual data entry costs a lot of time and money to perform. Automation allows organizations to massively reduce the cost of data entry while also preventing the additional work and cost caused by data entry errors.
- Better data quality: with higher accuracy than humans, document parsers improve the overall data quality, which in turn helps businesses to make better decisions (for example, optimizing the procurement process) and prevent costly mistakes (e.g. shipping the wrong item or quantity to a customer).
- Workflow automation: thanks to document parsing, entire workflows can be not only streamlined but automated. By integrating your document parser with the applications that you use (like a CRM or ERP), data can flow seamlessly from documents to your parser and then to your systems.
Now that we’ve established the key benefits of using document parsers, it’s time to jump into the document parsing comparison of AI-trained and user-defined parsers.
AI-trained document parsers
As the name indicates, AI-trained document parsers rely on AI to extract data from documents. To be more specific, this type of document parser leverages AI technologies such as machine learning, OCR (Optical Character Recognition), and NLP among others to process documents, understand the data inside, and extract it in a structured state.
Here’s what makes AI-trained parsers worth considering.
AI-trained data extraction
Generally speaking, AI-trained document parsers work like this: you upload one or several documents and the solution processes them. The AI then shows you the extracted data fields and you can edit the results to rectify inaccuracies. Once you validate the model, you can feed it new documents and it will extract the data points as instructed.
Keep in mind, however, that AI-trained parsers work best for commonly used documents like invoices. They may encounter difficulties when processing non-standard documents or new batches of documents with a somewhat different layout than the previous ones.
That said, after the AI model is trained on a large enough dataset, it can reach an accuracy level high enough to automate the entire data extraction process without the need for human involvement.
Advanced data processing
AI-trained document parsers have sometimes additional features that use machine learning to go beyond data extraction and extend into advanced data processing. Some of the most notable data processing features include:
- Document classification: classifying documents into categories based on their contents.
- Data validation: detecting duplicate or fraudulent data and notifying the user so that they can take the appropriate measures.
- Data analysis: analyzing large volumes of data to surface insights that inform decision-making (e.g. sales trends, highest-converting lead sources, etc.).
Depending on the industry you operate in and your data processing needs, these features may or may not be needed.
It’s also worth noting that sometimes, implementing an AI-trained parser can be a rather lengthy and costly process for small businesses or organizations. If you prefer using a document parser that focuses on quick and accurate data extraction, you might want to try user-defined parsers.
User-defined document parsing
User-defined parsers follow a rule-based approach where the user defines the parsing rules. These are the instructions that the parser’s algorithms will follow in order to identify each data field and extract it.
User-defined data extraction
Like AI-trained parsers, you can automate data entry with excellent accuracy to save time, effort, and money — a boon for any organization in which processes are held back by manual document processing.
If you’re wondering if the process of setting up rules is complicated, well, in most cases it’s not. User-defined document parsers have an intuitive point-and-click interface where you can easily select data fields on your document, choose from a dropdown list of parsing parameters, and so on. The best parsers will include a library of templates for common use cases like invoices or shipping orders. These templates will include pre-set rules so the setup process is even simpler and quicker. Plus, you can always request assistance from the parser’s support team.
But the biggest advantage of using a user-defined document parser is that you gain full control over the data extraction process.
Customize the data extraction process for any scenario
The best document parsers will provide users with an extensive array of parameters to configure parsing rules for practically any scenario. This way, you can get data exactly how you want it — you just have to take a bit of time to configure the parsing rules by yourself.
The user-defined parser can isolate a data field from the rest of a document in a variety of ways. For example, a parsing rule could focus on a specific area on a document where a table is located. The parser can also search the whole document for specific keywords and extract them. You can even tweak the formatting of some data fields like phone numbers or dates.
Furthermore, you can build multiple parsers for different use cases within your organization, from invoices to bank statements, forms, etc. Likewise, you can set different parsers for different document sources, and have all the extracted data land in one single database where everything is neatly organized and ready to be used. So as you can imagine, one user-defined document parsing solution can handle all your data extraction needs.
Final thoughts
To summarize this document parsing comparison, AI-trained document parsers leverage machine learning algorithms to extract data from documents, whereas user-defined document parsers do this with user-defined parsing rules. While AI-trained parsers can offer more possibilities for data processing, such as data validation or analysis, they may lack the thorough customizability allowed by user-defined parsers.
Both types of document parsers have their advantages and are suitable for different scenarios depending on the complexity of the documents, the level of accuracy required, and the desired level of control over the parsing process. We recommend that you try both types to determine which one works best for your needs.
If you’d rather customize your document parser thoroughly once and let it handle all incoming documents afterwards, try Docparser. Docparser is a leading user-defined document parser that works great as an alternative to AI-trained parsers.
