Analyze easily document files with AI: Optical Character Recognition OCR

Intro:

Definition:

History:

Use cases:

  • healthcare: digitize a patient’s entire medical history: health reports, X-rays, disease history, treatment tracking, diagnoses, hospital records, insurance coverage, payments
  • banking: leverages image processing to reliably convert scanned documents from images into searchable PDF files, allowing for specific information retrieval with keyword search.
  • legal: scanning, storage, and preservation in searchable databases are now possible for all printed documents: affidavits, judgments, declarations, notices, wills, etc.
  • supply chain: read instantaneous batch codes, expiration dates and serial numbers
  • insurance: claims processing can be automated by OCR and supporting technologies

The Multi cloud approac

  • First option: multiple open source OCR engines exist, they are free to use. Some of them can be performant but it can be complex to set up and use. Using an open source AI library requires data science expertise. Moreover, you will need to set up a server internally to run open source engines.
  • Second option: you can use engines from your cloud provider. Actually, cloud providers like Google Cloud, AWS, Microsoft Azure, Alibaba Cloud or IBM Watson are all providing multiple AI engines including OCR. This option looks very easy because you can stay in a known environment where you might have abilities in your company and the engine is ready-to-use.
  • you may not know every performant providers on the market
  • you need to subscribe and contract with all providers
  • you need to master each providers API documentation
  • you need to check their pricings
  • You need to process data in each engine to realize the benchmark

Test:

Eden AI python SDK for OCR
Eden AI python SDK result for OCR
Eden AI Platform for OCR

Conclusion:

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