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What are document processing applications?

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Document processing applications use machine learning and artificial intelligence (AI) to extract data from documents and forms. They take data, such as information from invoices, receipts, and delivery orders in email or paper form, digitize it, and store it in a structured database format. That data is then imported into a target system, such as an enterprise resource planning (ERP) or customer relationship management (CRM) solution. The efficiencies that document processing applications provide can have a major impact on how businesses save money, increase productivity, and free employees from repetitive low-value, error-prone tasks.

 

Finding a solution for digitizing volumes of paper-based documents is a challenge many businesses will face at some point. Document processing applications deliver great efficiencies in this area by not only eliminating the labor-intensive task of manual data entry, but also by giving businesses insights on how to get more from their data.

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How does document processing work?

 

Document processing is built on the foundation of optical character recognition (OCR), machine learning, and robotic process automation (RPA). These three elements are essentially able to interpret and understand information similar to the way humans would.

 

  • OCR recognizes printed, written, or typed text from scanned documents or images. It identifies light and dark areas in the scanned content and searches for letters or digits, which are then categorized based on patterns or features.
  • Machine learning creates algorithms that learn from patterns and context in documents. As it processes more and more information, machine learning learns from all the use cases it encounters, becoming smarter and more efficient in how it makes decisions.
  • RPA uses bots that execute on rules and instructions they receive to automate repetitive tasks. By using advanced text recognition capabilities, RPA can quickly process data from multiple sources.

 

With OCR, machine learning, and RPA working together, document processing typically follows these steps:

Collection

 

Data from sources such as paper documents, PDFs, emails, and electronic forms is scanned and digitized.

 

Preprocessing

 

The quality and accuracy of scanned data is improved by such things as correcting skewed angles, decreasing noise by eliminating any background spots or marks, and cropping unwanted outer areas from images.

 

Classification

 

Documents are separated into different categories based on their format, content, and type, which helps to improve the extraction and archiving of data.

 

Extraction

 

A crucial step in the process in which OCR extracts data from documents and defines what types need to be translated (e.g., names, numbers, dates, handwritten text).

 

Validation

 

RPA checks and verifies all data before moving it into relevant systems, databases, and workstreams. Any inaccuracies are flagged at this stage for manual review and correction.

 

Integration

 

Once all other processes are run, the data is sent to the relevant databases and repositories via application programming interfaces.

What is deep learning document analysis?

 

Deep learning documentation integrates with document processing by relying on the capabilities of neural networks to recognize patterns in data, particularly for document and layout analysis, text identification, and document retrieval. Much like how a human brain learns, neural networks gather information on many layers by acquiring more and more information and knowledge, getting smarter and smarter as they go.

 

Deep learning document analysis uses its ability to power human-like AI through different neural network algorithms, chiefly convolutional and recurrent. Convolutional neural networks filter through images to detect every element within them, while recurrent neural networks are able to remember data points, which shapes their ability to predict future outcomes.

Benefits of automated document processing

 

Automated document processing improves business processes and increases team efficiency by delivering speed, accuracy, and scalability. It can have a far-reaching impact on how industries such as legal, real estate, healthcare, and banking improve their processes and bottom lines.

 

Key benefits of automated document processing include:

 

Fast retrieval:

 

Once documents are digitized, they’re accessible virtually anytime and anywhere for anybody authorized to view them.

 

Improved security and privacy

 

Businesses can encrypt their files and assign levels of security to protect their data against unintended users.

 

Time and cost savings:

 

By eliminating the time-consuming and expensive process of managing paper files, employees have more time to devote to business-critical objectives and be more productive.

 

Reduced risk of human error:

 

Without the need for manual data entry, document automation greatly improves the accuracy and quality of documents.

 

Increased collaboration:

 

Employees on different teams across departments can share and work on documents together, staying aware of status in real time.

 

Standardized templates:

 

Document automation allows for the standardization of templates and structures that can be applied to workflows on an ongoing basis.

How to choose a document processing software solution

 

Choosing a document processing solution depends on factors specific to your needs. One of the most important decisions to consider is whether you want your solution to be run in the cloud or onsite at your location. Cloud-based systems are hosted by a provider for a fee and automatically save all your data, making everything accessible online. An on-premises solution means you’ll use your own servers and storage, perform your own maintenance, and run your own backups.

 

Other important considerations for selecting a document processing solution include:

 

Searching:

 

It’s a good rule to have a wide variety of search options, including file name and type, content, and dates modified. It’s also good to be able to assign metadata and tags for organizing all your files.

 

Straightforward file structure:

 

It’s important that the file structure is easy to use and logical for all users.

 

Security:

 

The system should allow you to restrict access to sensitive documents and set permissions by user.

 

Simplicity:

 

All employees should be able to easily use the system free of confusion and without disruption to their daily tasks.

 

Integration:

 

Ensure you’re able to use the system with programs you’re already using, like your email client and customer relationship software.

 

Get started with your document processing transformation now

Microsoft Power Automate is an easy-to-use workflow optimization solution that enables your employees to create a document processing solution. Minimize repetitive, manual, time-consuming tasks and create more time for your teams to focus on strategic work with a single platform for automation.
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Frequently asked questions

  • Document processing applications provide an automated solution for digitizing large amounts of paper-based documents.
  • Document processing is based on machine learning and artificial intelligence, which work to extract data from documents and store it in a database.
  • Deep learning documentation analysis relies on the capabilities of neural networks, which learn and acquire knowledge similar to the way human brains do. The more information these networks acquire and learn from, the smarter they get as they process more information.
  • Increased productivity, reduced risk of human error, and improved scalability are some of the many benefits of automated document processing.
  • Start by assessing your current document workflow and determine what you want to improve on. Some of the key things you’ll want from a document processing solution include scanning capabilities, cloud storage, search functionality, document version control, and the ability to manage permissions.

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