AI Machine Learning: Intelligent Document Processing
Intelligent data capture and optical character recognition (OCR) solutions have been used by businesses to automate data extraction from documents. These innovations, however, fall short. It’s time for an intelligent document processing (IDP) solution that brings together the best AI technologies to speed up business process automation.
What is AI?
In general, AI excels at automating routine and repetitive tasks. To put it another way, it excels at optimization. Consider the following scenario: People whose jobs revolved around moving your product from their warehouse to your doorstep used to power Amazon Prime. That process follows a consistent algorithm that does not change from day to day. As a result, the warehouse’s repetitive and boring work could be optimized and delegated to robots.
What is Machine Learning?
While artificial intelligence (AI) is the broad science of mimicking human abilities, machine learning is a specific subset of AI that trains a machine how to learn.
In data science, an algorithm is a sequence of statistical processing steps. In machine learning, algorithms are ‘trained’ to find patterns and features in massive amounts of data in order to make decisions and predictions based on new data. The better the algorithm, the more accurate the decisions and predictions will become as it processes more data
We can expect more. As big data keeps getting bigger, as computing becomes more powerful and affordable, and as data scientists keep developing more capable algorithms, machine learning will drive greater and greater efficiency in our personal and work lives
How AI machine learning works in extracting document
Although more intermediary steps can be added, there are 5 main stages of AI-powered document processing:
- Importing data into system
- Data classification, tagging, indexing
- Optical Character Recognition
- Correct interpretation of symbols
The first step is to enter data into the system and process it according to the format it came in. Some documents are hard copies, while others are scanned images, and still others are in a semi-tabular format, such as fill-in pdfs or spreadsheets. A truly helpful processor would support different formats and be able to accurately extract information from each of them, or at the very least alert the user to any potential errors.
Data classification, tagging, indexing
Following that, the extracted data must be categorized, tagged, and divided into categories and components. When reading an invoice, for example, the machine must recognize all of the different items such as the vendor’s data, the buyer’s data, quantities, royalties, discounts, bought goods, and internal codes such as the contract number. Once the areas containing such data have been separated from the rest of the environment and
Performing optical character recognition
The third thing that needs to be done is the actual OCR (optical character recognition) of the input data. The goal at this point is to convert everything into a format that can be read by machines and easily edited by humans in case of errors.
Correct interpretation of symbols, in context
The real challenge is properly interpreting symbols and putting everything into context. Dots and commas, for example, are used to designate deciles and thousand-fold numerals. It’s critical to recognize which convention is used in each of the processed documents; otherwise, significant computational errors could occur.
The first role of AI is to inform the system of what it is looking at. Is a given document, for example, a receipt or an invoice? Creating templates and searching for matches is one solution, but this is a time-consuming and unreliable method since each merchant has their own forms and documents. A better approach is to teach the system to consider the context — this method has proven to be effective.
Last but not least, it is critical to make informed decisions based on the information gathered. This is the step that distinguishes conventional OCR from AI-powered OCR.
Until now, humans analyzed all data extracted from documents and produced reports and made decisions. The leap forward exists in the possibility of letting the algorithm do some of the job, such as sending reminders, making automatic payments when certain criteria are met, and responding to customers through chatbots, is a huge step forward.
Roboextract is equipped with AI machine learning and natural language processing to support your daily document processing. Roboextract helps enterprises to extract every documents in structured, or even unstructured forms. Rather than replacing employees, Robextract’s aim is to speed up human operators, giving businesses more flexibility and reliability for their customers and helping employees focus their attention on more complex tasks or tasks that require creativity. To get started, submit your request for a one-month free trial.