Data Capture

Document data capture also known as OCR (optical character recognition), in the context of programming and information technology, refers to the process of extracting relevant information from various types of documents, such as text files, images, PDFs, and scanned documents.

This process involves using software tools, algorithms, and creative techniques to automatically identify, extract, and organize data points and content from documents. The extracted data is then put in a logical order and format so it can be further processed, analyzed, and integrated into databases or other systems for various purposes.

Below are key components and steps involved in a proper document data capture system:

Input Documents

These can include a wide range of document types, such as invoices, receipts, contracts, forms, reports, emails, and more. The documents may be in different formats, such as plain text, images, PDFs. Handwritten notes can be read and, in some cases, it reads accurately if there is a specific format to the information. If the information is random, it is possible but is captured in a less accurate way and making it work takes a lot of time, cost and effort.

Scanning or Uploading

The documents are usually scanned or uploaded into our system which then ingests and processes them. The scanned documents go through optical character recognition (OCR) to convert images into actual editable text.


The documents often require preprocessing steps to enhance the accuracy of data extraction. This might involve noise reduction, image enhancement, and other techniques to make the content more legible and consistent. This process is always done automatically in a good system with no user intervention. The better the processing algorithms the more accurate the results.

Data Extraction

This is the core step where the software uses various algorithms and methods to identify and extract specific data points from the documents. For instance, if you’re dealing with invoices, the software identifies fields like invoice number, date, item descriptions, and amounts. It will even make sure the mathematical calculations are correct and point our errors. This is a true machine learning function that gets to know your documents and their patterns, the process is known as machine learning which is part of an AI process.

Data Validation

Extracted data may need to be validated for accuracy and consistency. Valida-tion rules can be applied to ensure that the captured data adheres to expected formats or ranges. Documents that pass the test go straight to the export and the ones that do not will go to the verification station to make sure all the input data is accurate.

Data Transformation/Export

Once the data is extracted and validated, it is transformed into a standardized format that can be easily processed and integrated into other systems. This could involve converting dates to a common format, normalizing text, or converting units. Our system has many output formats that are easily configured such as XML, Excel, CSV and more.

Data Integration

The captured and transformed data can be integrated into various systems or databases, such as customer relationship management (CRM) systems, enterprise resource planning (ERP) systems, or analytics platforms. This enables organizations to make informed decisions based on the extracted information.

Continuous Improvement

Our data capture system as mentioned employs machine learning techniques that improve accuracy over time. The system can learn from user feedback and adjustments to become better at accurately capturing data from similar documents in the future.

OCR’s general document data capture is a crucial process for automating the extraction of relevant information from a variety of documents. It streamlines business processes, reduces manual effort, and enables efficient data utilization for decision-making and analysis.

Get in touch and see how we can implement it in your business