As compared to the other open-sourced OCR engines, tesseract provides a good amount of accuracy in terms of text extraction. I also explored other OCR engines like Easy OCR, Keras OCR, but among them Tesseract proved to be the best. (At least for me) Review collected by and hosted on G2.com.

Working over tabular data is quite a tough task sometimes. Sometimes it completely misses the context of the statements and generates some random words. Definitely, that also depends upon the graphic quality of the documents. For that, I had to work a lot in the computer vision domain to get the documents ready so that the accuracy can be improved. One additional suggestion that I can give to the Tesseract community is to implement a user-friendly dictionary within Tesseract so that some non-tech guy can also use it very easily. I implemented an additional dictionary where I wrote all the domain-specific words so that while extracting text from any document, I can be able to improve its accuracy. Some of the computer vision attributes which I used were like Adaptive threshold, warp perspective, contrast. I also used a few libraries for autocorrection of the text which was extracted by Tesseract. I would suggest Tesseract to give out a feature of a Domain-specific dictionary where users can write some custom words which they believe would be there in the document. So that while extracting the text at least those domain-specific words can be fetched correctly. Review collected by and hosted on G2.com.