Why OCR fails? 5 Drawbacks and Limitations of Conventional OCR Engines



While conventional OCR engine is considered as a mainstay platform and might seem like the end-all, be-all solution for capturing data. It can be frustrating at times when the data is misread or not even be read. Inputting a document into an OCR doesn’t mean that it will actually output something without a hitch- forcing knowledge workers to manually work.

Why does OCR fail?

Many OCR engines fail to support and understand the complexity of the input data in a given document. For example, if the input document is a form then the OCR might identify the text but may not recognize text over a line or the text in blocks. This may result in unexpected output.


Originally published at infrrd.ai 

Comments

Popular posts from this blog

What Mick Jagger Taught Me about Data Extraction from Tables

Streamline work processes with a modern data extraction method

Eliminate your OCR and Manual Data Entry Bottleneck