Quick Guide On the 5 Steps of Document Data Capture
The invaluable information contained in data is of no use if it cannot be comprehended precisely. All thanks to Document Data Capture solutions that pick up data trapped in different locations, format it, and organize it in a more structured form, favoring seamless analysis. A quick insight into the steps of document data capture will provide you with a clearer picture of how the process works.
Understanding Document Data Capture
Technically capturing data from a document is called Document Data Capture. Different tools and software enable businesses and individuals to capture data from PDFs, invoices, old magazines, newspapers, scanned files, images, electronic files, and more.
It is the smarter way to process documents and extract information rather than spending hours in manual entry or extraction. Moreover, automated solutions automate decisions and help you access better data.
5 Steps of Document Data Capture
Document management solutions simplify the entire process of data capturing by automating the most time-consuming steps, reducing manual data capture, speeding up the workflow faster, and making it more efficient and error-free.
The key steps involved in the process are as follows:
The first step involves document capturing. It employs ICR, OCR, and other technologies to scan physical documents.
OCR stands for Optical Character Recognition. The technology extracts texts and characters from documents or images by scanning them, converts the information into a machine-readable form and further processes it.
ICR, or Intelligent Character Recognition is basically a subset of OCR that specifically scans handwritten documents. It is more advanced as it identifies data from varied handwriting styles and converts it into a computerized format.
For e-documents, inbuilt integrations of intelligent document processing solutions help import data.
The document data capture software pre-processes the captured document and employs measures to improve its quality. For instance, they check the alignment of scanned images of a hand-filled invoice, alter the brightness if document data is underexposed, and make other required edits to make the captured data more readable and accurate.
After pre-processing, begins the real work on the captured data. The software identifies the information in the documents and classifies it accordingly. For instance, a bank’s KYC update form usually contains documents, including filled form fields, identity proofs, residence proofs, and more. The tools and software recognize every document’s purpose and send it to the respective workflow.
The most important step in document data capture is extraction. The crucial information is pulled from the pre-processed and classified documents. It is then entered into the pertinent database for analysis or any other further use. This step will also describe the extracted data type, such as names, numbers, addresses, or other details.
The last step involves verification of the retrieved information for authenticity and accuracy. The document data capture software utilizes external databases and glossaries to check the captured data for discrepancies. If it comes across any inaccuracy, it directs the file toward human evaluation and corrections. This step plays a significant role in keeping the software database updated and improving its AI algorithms.
Click on this link to discover how document data capture can help you with your tasks and simplify data analysis.
Concludingly, document data capture solutions are a blessing to data-driven organizations and individuals. It’s time to switch to automation wherever possible in order to be more productive. Make accurate decisions from unstructured documents with the help of AI software with Intelligent OCR technology. From pay stubs to invoices to bank statements, transform regular data into actionable ones with up to 70% reduction in processing costs and a more than 50% increase in efficiency.