Checkout how Intelligent Data Capture displaces Traditional OCR | AIBridgeML
  • byAIBridgeML
Information extraction from documents and invoices has been revolutionized with the advent of modern tools and technologies. The conventional OCR methods of data capture have evolved into capabilities that can scan documents or process underlying information at a go. Just capturing information with no analytical capabilities is not ideally beneficial. Further, an orthodox manual invoice process is all the more prone to errors.
Modern means of invoice capture may handle different forms such as handwritten text, and capture the same into the desired format. Intelligent Data Capture can extract information from various digital assets such as texts, files, and scanned images. Thus, modern data capture tools capture images, logos, and time-stamps. Data capture is associated with safety and redundancy risks. Innovative data extraction tools powered by AI offer various value-added benefits that revolutionize enterprise business functions.
Automated Data Capture: How it works?
The entire process involves digitizing images or scanned documents and converting pictures to text. Firms can achieve cognitive data capture with the help of robust management tools for extracting key line items for the business that might involve Date format. These may include the Payment Due Date, Check-in Date, or the Renewal Date. The handling of unstructured information embedded within documents has been a challenge, and firms can overcome the same by leveraging intelligent data capture.
Data Capture System would extract data from documents or images using intelligent agents and networks. Different sequences of characters translate into respective text or date formats for efficient use by stakeholders. Certain challenges remain in automating invoice processing such as those related to costs. There are overhead costs towards deploying a valuable data capture solution and it might put firms in a dilemma; unless there is a feasible plan to store, transmit, and use data generated in various forms.
Collating Personnel Data
The modern data capture tools will eliminate the shortcomings around data extraction from invoices of different formats, irrespective of the layout or the source of the document. This is categorically true of functional areas such as Insurance, Legal, Auditing, Finance, and more. For instance, a dynamic process can extract the name and address of a fund manager from the huge data pertaining to the transactions of a BFSI firm. It could also store the same data in desired format on local file system or the enterprise server, based on specific needs. This would save costs, improve processes, and accelerate ROI. The modern data capture tools have thus transformed business processes in the desired path.
Data Extraction with RPA
Newly-developed modern Intelligent Data Capture systems leverage RPA and intelligent agents using UiPath capabilities. The data extraction software overcomes any inconsistency in the templates while extracting just the right information from these documents. These can also transmit only the needed information across different touch-points throughout the business workflow. 
AI-powered processing ensures speed as well as improves the scope of trained systems, thereby reproducing reports at the rate of hundreds – or even thousands of documents – within a second or so. It boosts turnaround time and streamlines the entire operational processes across the organization.
  • Boost ROI with greater operational efficiency and minimal data redundancy
  • File reading, extraction, and validation with robust data extraction software
  • Eliminating tedious human labor that also involved risks and intrinsic errors
  • Invoice automation for a large number of transactions across the enterprise
  • Intelligent agents reduce the error rates by performing all predictable tasks
  • Data Capture tool to enable seamless data transfer to the primary ERP system
  • Improves data accuracy with seamless and intelligent document processing
  • Greater edge over conventional methods that relied on OCR methodology
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