Business Development Through Different Approaches Of Automated Data Capture
Among the advantages of automated data capture include time and cost savings, reduced human involvement in data gathering and processing, improved consumer experience, facilitated search, and more.
Fremont, CA: Digitization now pervades all business sectors. Every day, humans generate 2.5 quintillion bytes of unstructured data. Whether audio, video, or text, big data can provide commercial value when painstakingly collected, recognized, and processed. But no matter how smart machines are, they can't absorb and analyze the information as people do. The data must be digitized to train machine learning and deep learning algorithms. So AI-based solutions might understand it.
OCR, OMR, and ICR
Optical character recognition
Optical character recognition (OCR) is a well-proven and well-established technique. After disrupting the traditional approach to document management, technology is still as vital as ever. It is frequently used in fields such as logistics, healthcare, finance, banking, governance, and more as a great option for digitizing enormous amounts of paper and electronic records. Multipurpose OCR systems effectively lower data capture costs, automate regular manual processes, and replace human employees with repetitive duties. Even though human inspection is required, especially when working with legal documents and financial reports, the solution is a must for cost-effective document management.
Optical mark recognition
Optical mark recognition (OMR) is another method of document management. The technology is frequently used to speed up and simplify the capture of data marked by humans. For example, results of polls, multiple-choice tests, customer feedback, and surveys. The technology finds the location and recognizes handwritten marks several times faster than human employees after scanning the documents. The tech-based strategy promotes company workflow automation by allowing machines to complete normal operations in a time and resource-efficient manner.
Intelligent character recognition
The goal of intelligent character recognition (ICR) is to solve more complex problems. Teaching machines can now process handwritten documents using this technology. The amount of accuracy might range from 50 percent to 70 percent, depending on typefaces and styles, block letters, or cursive handwriting. Further training of the algorithm on large datasets of specialized data can improve this rate.