Using Tesseract OCR with Python

We are trying automate the entire loan application and processing. So, this involved a lot of character recognition stuff as our target group have their financial documents as hard copies. Helping them autofill their information would make their task easier as well as avoid human errors while typing. So, after reading a few articles, I first designed a OCR using google’s OCR library tesseract. The classifier produced good results when it came to reading standardised documents. But, as the complexity of the document grew, such as reading a cheque, it became challenging to achieve considerable accuracy. So, to avoid the complexities of training a custom classifier and deploying it on the cloud (which would require significant amount of computations) we decided to use Microsoft Azure’s Vision API. It provided us the coordinates of all the texts and all we had to do was look for texts similar to an Account number and IFSC from a cheque book. Using some regex it was easy to find closely matching strings. Later we extended this to read bank statements, this is where even Azure failed to read everything in the image. We had tried google vision’s API earlier but the output wasn’t satisfactory. So, decided to work on making the image more readable. I came across a lot of image filters whose main motive was to convert the image to only black and white, no other colours. I tried out a lot of them, some of them were the mean, median and gaussian thresholding. The one which worked best for us was a custom designed filter using Otsu’s Thresholding principle.

If you plan on using tesseract definitely try out their 4.0 beta, which uses LSTMs.

https://github.com/tesseract-ocr/tesseract/wiki/4.0-with-LST...

Note that pytesseract has no bindings with tesseract's API, it merely uses the tesseract command line tool and communicates with it via stdout/stdin.

If you want native and complete access to tesseract's API you can use tesserocr: https://github.com/sirfz/tesserocr

Teaseract is ok printed material that's neatly organized, but other than that it seems the only other programmatic ocr is google cloud vision. It's a hundred times better, but unfortunately I need to OCR documents I can't contractual show the mighty G

I'm combining opencv and pytesseract in order to process some scanned forms. Doing this I was able to link 70k forms to a database previously filled by professional typists. Now I have a huge data set I can use to train ML algorithms, I'm experimenting with several of them.

I have no formal training in CV, so my impression is that recognition is relatively easy, the hard thing is the preprocessing need in order to normalize images.

Few days ago I've written a python code to process a PDF file with Tesseract or Google Vision API: https://github.com/lucab85/PDFtoTXT

Is there a way to combine the character-level OCR with knowledge of the English dictionary? Something like `pregrarrmung` should be able to map to 'programming' especially with n-gram context of pregrarrmung experience.

If you really want to test an OCR tools, try scanning something like an XBox Live membership card. The fonts on those cards seems to be specifically designed to mess with OCR.

I used tesseract/pytesseract, almost perfect pre processing using blur, otsu etc, But for get good results, you need big images, 300 dpi+ are needed, The big images make it is too slow, Maybe i should have try segmentation the caracters before using the ocr, I endeup making my ocr from scratch, using averages etc, and it is almost instant, and i am happy with it.

Here are two of my blog post in regards to using OCR to bypass some security mechanisms:

In short: It's a python code where you press one button and it will take a screen shot, crop the image, decode it, and type in at over 900+ rpm.

https://anthonys.io/ocr-engine-playground/

To see how it is in action without the OCR functions:

https://anthonys.io/keybr-com-multiplayer-cheater/

I created a set of Python bindings to Tesseract a couple of years ago. While not complete, they would likely make a great starting point for anyone wanting to interface with it at a deeper level. Reminds me, I should do some modernization work on it. https://github.com/blindsightcorp/tesserpy

(Hopefully) Related question: What is the state of the art in OCR on photographs? Is there something like the inception model for OCR?

A tangentially related question: Will OpenCV (used under the hood in this example) continue to support Python bindings in future versions?

Does anyone know if there are tools to detect Photoshopped/forged/modified images?

for preprocessing you can just use pillow for thresholding,rgb<-> gray conversions etc. While opencv gives much more option its a heavy library to use for this kind of functionality.

I am surprised at the lack of any mention of SikuliX.

I know Tesseract is OS but when I tried it, it was nearly useless.

I use Abbyy with WINE. But a native Linux shell version of Abbyy is available: http://www.ocr4linux.com/en:start