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How AI And Deep Learning Algorithms Deliver OCR Accuracy for Business

The technology that enables us to extract information from scanned images of printed or handwritten documents is called Optical Character Recognition or OCR. It has applications for the processing of business contracts, receipts, utility bills, passports, product sheets, handwritten forms, bank statements, and a variety of PDF documents generated by businesses. 

Accurate and reliable data extraction plays an important role in finance, accounting, electronic health record updates, insurance claims processing, personal and national security, logistics documentation, and legal documentation. The extraction accuracy is extremely important because OCR is often used to convert documents such as passports, invoices, or receipts, where every bit of information is vital and errors can prove costly.

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Everything You Need to Know About Building an OCR Scanner From Scratch

Optical Character Recognition (OCR) tools have come a long way since their introduction in the early 1990s. The ability of OCR software to convert different types of documents such as PDFs, files, or images into editable and easily storable format has made corporate tasks effortless. Not only this, but it’s ability to decipher a variety of languages and symbols gives Infrrd OCR Scanner an edge over ordinary scanners.

However, building a technology like this isn’t a cakewalk. It requires an understanding of machine learning and computer vision algorithms. The main challenge one can face is identifying each character and word. So in order to tackle this problem we’re listing some of the steps through which building an OCR scanner will become much more clearer. Here we go:

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Image Recognition for Product and Shelf Monitoring and Analysis

With the e-commerce boom, entrepreneurs have learned that conventional strategies of visual merchandising or sales promotions won’t be able to sustain profits in the cutthroat CPG industry. Many retailers are already implementing AI and image recognition to deliver the next level of customer experience, bringing the dawn of a new era for the retail industry. According to Gartner, by 2020, 85% of customer interactions in the retail industry will be managed by AI. Product discovery, product recommendations, and trend analysis are some areas for the implementation of computer vision and image recognition. This article elaborates on how image recognition can be implemented by retail and CPG companies.

1. Auditing Product Placement

Customers are making key buying decisions at store shelves and companies have to use technology to stay ahead of the fierce competition or face extinction. Gathering key consumer information helps companies understand their needs better. Shelf recognition using computer vision digitizes store checks and is important in gathering key consumer information through AI.