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In today's fast-paced digital age, content is king, and businesses are continuously seeking efficient ways to capture and process large volumes of information. The rise of Artificial Intelligence (AI) has brought promising solutions to automate content capture processes, saving time, reducing errors, and enhancing productivity. However, like any technology, AI-powered content capture comes with its own set of challenges. In this forum, let's explore the hurdles faced by organizations and discuss potential solutions to harness the full potential of AI in content capture.
Conclusion: Automating content capture processes with AI offers immense potential for businesses to streamline operations and gain a competitive edge. However, addressing the challenges of data quality, unstructured data, scalability, and security is crucial to successfully harness the power of AI. By implementing the suggested solutions and staying updated with the latest advancements in AI technology, organizations can overcome these challenges and unlock the full benefits of automating content capture with AI. Let's continue to share our insights and experiences to foster a more efficient and secure content capture ecosystem.
by Motaher Hossain
Has anybody successfully implemented Datacap with ADP or other AI based OCR engines ?
Kindly share more about your implementation and experience.
Thanks & Regards,
Sathish A Rajan
So ADP is more than an OCR Engine, it's doing full OCR, Classify, Extract. There's an ootb ruleset to enable Datacap to connect to it by @Scott Sumner-Moore GitHub - IBM/Datacap-ADP-Connector: Roundtrip connector from Datacap to ADP. It's straightforward to use and integrate.
We have also done some integrations building custom rulesets for Google, Amazon and Microsoft's computer vision based text extraction engines. Those are trickier to integrate, though, because you need to do the work to translate the text output into something that Datacap can understand.