How does Computer Vision address data extraction challenges?

Managing unstructured data has emerged as the biggest hurdle for enterprises. Due to various structural data discrepancies, advanced technologies like Intelligent Automation and AI have become at the top of the agenda for enterprises as it enhances quality and reduces response times with fewer resources. Through analytics-based insights, it identifies the bottlenecks, traces the improvement areas, and creates bots to enable companies to offer an end-to-end business solution to the customers. However, this process can only work efficiently if the data is available in an actionable manner.

Against this backdrop, the need for digitization to capture, extract and process data seamlessly has grown exponentially. To tackle these document data challenges and unlock business value, enterprises have begun implementing some of the latest digitization technologies; one such technology is Computer Vision.

Computer Vision is modeled on human capabilities to identify, verify, and examine images and videos. Computer Vision applications play a crucial role in converting unstructured data into actionable data.

Why should enterprises go beyond OCR?

Optical Character Recognition (OCR) plays a substantial role in automating business processes through its capability to replace manual labor with software-driven processing. Despite OCR being beneficial, it has its own shortcomings while handling complex processes. The key reason for this is that many documents are in inconsistent format, and OCR becomes more effective when documents are coherent data for comfortable digital accessibility. This is where Computer Vision steps in to resolve the problem.

Computer Vision utilizes an innovative and smarter way of scanning and detecting critical data in document form. When combined with automation and OCR, Computer Vision drives substantial performance improvement. Primarily, Computer Vision identifies those parts of interest in each document. This information then passes on to the OCR engine, where the information can be transformed into a structured format.

Once Computer Vision is implemented, it correctly identifies and demarcates the required object; OCR can then be deployed to extract and convert the right data with higher accuracy. In this way, Computer vision overcomes OCR’s limitations by acting as an in-between or preparatory step before text or data extraction.

What is Computer Vision?

Computer Vision is the practice of seeing, reading, and identifying objects or information in an unstructured setup by implementing Artificial Intelligence. Although OCR is a part of Computer Vision, it is not as effective as Computer Vision’s capability to outdo human accuracy in rapidly identifying and responding to visual inputs.

Computer Vision works by digesting massive quantities of data on related images to recognize specific characteristics and patterns. As it can enable the structuring of digital data, it enables OCR, which is still the most convenient way of converting data into digital forms.

How does Computer Vision work?

Computer Vision mimics the human brain and helps decode and make sense of visual data by using underlying patterns. For example, OCR  is applied for seamless processing, but it becomes unproductive when used for images. However, AI and Computer Vision can use pattern recognition to identify documents and organize them together without human interference. Once recognized, Machine Vision and ML applications can be instructed to obtain data.

Some primary purposes of Computer Vision:

Need, significance, and benefits of Computer Vision

Applications are overcrowded with all categories of texts, videos, and photos. The same is the case with documents of enterprises in almost every industry. Hence, it has become mandatory for organizations to capture, analyze, and understand the requirements by identifying them from the available visual content and converting them into structured data. This is where the role of Computer Vision with AI and NLP becomes even more significant for ensuring moderation and examining the online visual content.

With the assistance of Computer Vision, the digital transformation journey is likely to succeed as most of the enterprise data is structured. Subsequently, businesses will need help to optimize digitization. Therefore, converting unstructured data into a digital format that can be identified, manipulated, and processed is crucial. Hence, the significance of computer vision in aiding enterprise digitization becomes even more prominent due to its advanced-level benefits.

Some of the benefits of Computer Vision are:

Assisting RPA: RPA relies on access to consistent data sources to run automated solutions. Hence, optimizing RPA depends on essentially translating the data into a digital format.

Diminishing risk: There are various risks associated with regard to data privacy management due to increasing cybercrimes. Hence, regulations are being made to safeguard certain forms of data. For instance, identifying digital customer data and its storage location is critical. However, this threat can be reduced by applying Computer Vision.

Drives innovation: The data obtained from scrutinizing the enterprise data will be lost if this data is not saved in a digitally identifiable and accessible format. After the data is identified, this uncovers the consumption trends, patterns, consumer preferences, challenges, and other factors that can help the management to drive enhanced decision-making. The sheer size of these data pools, or lakes, is tremendous. Hence, digital conversion is crucial.

Boosting customer experience: Assembling all the customer’s interaction or exposure data will only ensure that the experience is optimized and enhanced.

Increasing content extraction accuracy: Computer Vision expands output and reduces the number of transactions routed for exception management.

Conclusion

Computer Vision is fast emerging as the ‘go-after’ solution garnering much attention across all the major industries. Due to this, it will likely be able to operate on a broader ecosphere of content data in the coming times.

In the last few years, there has been an infusion of new products that demand the services of Computer Vision and AI, with companies applying Computer Vision for process optimization to identify processes to be automated with better precision and superior pace.

Additionally, with organizations increasingly embracing technologies like the Internet of things and AI across industries, enterprises have already begun to pay urgent attention to Computer Vision. Therefore, this momentum of companies leveraging various benefits from the adoption of Computer Vision Applications will surely continue in the times ahead.