We shouldn’t apply all preprocessing logic to one document which will decrease the accuracy. For instance we have to apply filter to either increase blur effect or decrease blur effect based on how the image document is generated. Although some of the preprocessing logic are common (Increase dpi, grayscale,skewing or deskewing, e.t.c), we have to do a lot of preprocessing specific to document noise type. Since Tesseract still have error on determining financial number/currency/kyc information from document, it might have a huge impact for errors in finance domain.Īlso before feeding input image documents to Tesseract we have to preprocess documents. For example implementing OCR based solution to banking domain will have restriction. Tesseract 4.0 gives decent accuracy for well scanned image documents but still that accuracy might not be enough for gaining business traction. Will Tesseract help with all problems and all domains? Recently neural net based OCR engine mode is made available on Tesseract 4.0 which gives improved accuracy for image documents that have high noise (Not well scanned document). Tesseract is actively developed by a community and it is supported by Google (As of June 2019). When someone wants to get started with an open source OCR to build an MVP, they can pick Tesseract as their first try.
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Tesseract is the best OCR software open source.
The challenges faced in the process of identifying an OCR and doing entity extraction are: If you want to prioritize OCR solution which has less restriction for gaining business traction?
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So it is essential to have a robust document comprehension system (OCR + NER).īut, unfortunately none of the commercial offerings are a silver bullet solution for practical RPA implementations. In any business workflow, handling documents of different types and quality is an integral part.