Document capture software is an application that can automate the process of scanning paper documents or importing electronic documents for capturing the relevant information for further operations. These tools can collect unstructured forms of data, turn them into actionable information to be used in specific business functions or intents, and store them in databases for future reference.

Here is how document capture software works:

• Documents are imported to document capture software.
• The text is transformed into a readable format by deskewing and cleaning the image and improving image quality.
• The software reads and captures unstructured data that passes predefined tolerance levels. If a document fails, it is sent for manual verification.
• The collected unstructured data is converted to structured data by leveraging machine learning algorithms. The data is classified and appropriately validated in this step.
• The data is transferred to the database for further processes.
• If needed, the captured data can be processed for further tasks like document generation. You can read more about this in our document automation guide.

Most common business documents include:

Finance Operations
• Procure-to-Pay
• Offers
• Invoices
• Bill of lading: Necessary for matching goods received and invoices received in IRGRC (invoices received goods received clearing)
• Order-to-Cash
• Order forms
HR Operations
• Travel and expense management
• Receipts
• Invoices for individual spending
• Tickets
• CV Screening
• CVs
Legal Processes
• Tax Statements
• Legal Contracts
Healthcare
• Prescriptions
• Medical records
Other Processes
• Loan Application forms
• Payslips
• W2 forms

The main benefits include:

• Faster processes
• Reduced costs
• Reduced errors
• Improved customer satisfaction
• Improved security
• Better decision making

To read more about how document capture tools achieve these benefits, feel free to read the related section of our in-depth document capture guide.

Typical document capture use cases include:

• Accounts Payable: In these processes, document capture tools can provide invoice automation and process invoice data like line item information, delivery dates, shipping costs, and discounts. To learn more about accounts payable automation, you can also read our in-depth guide.
• Order Management: Document capture tools can handle a wide range of documents that order management departments use to carry out their activities. To learn more about order management, feel free to read our related article.
• Auditing: To identify risks in real-time and identify compliance issues, companies can benefit from document capture tools.
• Loan Applications: The software can provide automated examinations of payslips and bank statements of applicants to accelerate the processes.
• Analytics & BI: Data stored in forms is not always captured by the business as manual data capture is prohibitively expensive. Analytics units can process historical documents, capture data and run analyses to gain insights on how the business is progressing over time and identify improvement opportunities.

For more use cases, you can visit the related section of our in-depth document capture guide.

The ideal document capture tool for your company should:

• recognize a well-scanned document accurately and extract the data in structured data format
• be robust in cases of inadequate image quality and handwriting,
• be accurate in estimating its own accuracy.

Extracted data needs to come with confidence scores to enable STP. If scores are not accurate, you may auto process documents that need human in the loop resulting in mistakes or you may require human operators to look at documents that are already extracted correctly

Considering these factors, you should first decide on what kind of document capture tool you need. For example, some vendors can provide better results in handwritten documents while they might not be accurate enough in formatting. Then, you should create a shortlist of possible vendors based on your requirements. Besides software performance, you might also want to consider the following items to make a final decision:

• Accuracy level of the solution evaluated based on a statistically significant, representative sample set from your documents
• User-friendly interface
• Cost and timeline of implementation
• Ability to integrate with your current ECM (Enterprise Content Management) tools so you can implement the new solution without changing existing workflows. This is only relevant for companies that already rolled out and are satisfied with the performance of their ECM system
• Vendor experience
• Vendor support
• Conforming to other requirements such as data privacy, security, auditability, scalability, monitoring/alerting capabilities etc.

Document capture software leverages the following technologies to perform tasks:

• Optical Character Recognition (OCR): Document capture tools need to recognize text in every document. To do that, OCR plays a critical role by benefiting from computer vision to text recognition and deep learning algorithms for identifying each character. You can read more about OCR in our in-depth guide.
• Neural network algorithms: To classify the unstructured data that is captured from scanned documents, neural network algorithms are used. By continuously being used, document capture tools can increase their accuracy levels in time. These algorithms are used in OCR for precise character recognition, as well. With the rise of deep learning, deep learning architectures are commonly used in neural networks in this field.
• Natural Language Processing (NLP) Algorithms: As part of entity recognition, NLP is used to process and understand natural language text and extract captured information within the documents.
• Word Embedding: By clustering similar words together, document capture tools can classify different types of documents fastly and with reduced errors.

While document capture tools manage a critical part of business operations by handling repetitive, low-skill tasks, the main challenge about these tools is to capture relevant data accurately. While document capture tools can work with high accuracy with typed documents today, they still require human in the loop to avoid any recognition errors.

Yet, active research on machine learning continues to overcome this challenge. Today, this research is mostly focused on handwritten documents and cursive texts, as they are harder to identify. In the future, we expect document capture tools to handle these tasks successfully and without any human intervention. You can read more about this in our current state of OCR technology article.

Besides improving data capture processes, converting unstructured data to structured data is still a developing process. While this process requires AI and machine learning algorithms to structure data accurately, many tools still require human intervention to avoid errors today. Both tech giants like Amazon and startups like Hypatos are investing in machine learning to improve the assignment of text to data entities and therefore converting images more accurately into structured data. As a result, we expect more accurate processes in the future's document capture tools.