I'm working on a project that involves extracting text scientific papers stored in PDF format. For most papers, this is accomplished quite easily using PDFMiner, but some older papers store their text as large images. In essence, a paper is scanned and that image file (typically PNG or JPEG) comprises the entire page.
I tried using the Tesseract engine through it's python-tesseract bindings, but the results are quite disappointing.
Before diving into the questions I have with this library, I would like to mention that I'm open to suggestions for OCR libraries. There seem to be few native python solutions.
Here is one such image (JPEG) on which I am trying to extract text. I the exact code provided in the example snippets on the python-tesseract google code page I linked to above. I should mention that the documentation is a bit sparse, so it's quite possible that one of the many options in my code is misconfigured. Any advice (or links to in-depth tutorials) would be much appreciated.
Here is the output from my attempt at OCR.
My questions are as follows:
EDIT: For simplicity, here is the code I used.
import tesseract api = tesseract.TessBaseAPI() api.Init(".","eng",tesseract.OEM_DEFAULT) api.SetPageSegMode(tesseract.PSM_AUTO) mImgFile = "eurotext.jpg" mBuffer=open(mImgFile,"rb").read() result = tesseract.ProcessPagesBuffer(mBuffer,len(mBuffer),api) print "result(ProcessPagesBuffer)=",result
And here is the alterative code (whose results are not shown in this question, although the performance appears to be quite similar).
import cv2.cv as cv import tesseract api = tesseract.TessBaseAPI() api.Init(".","eng",tesseract.OEM_DEFAULT) api.SetPageSegMode(tesseract.PSM_AUTO) image=cv.LoadImage("eurotext.jpg", cv.CV_LOAD_IMAGE_GRAYSCALE) tesseract.SetCvImage(image,api) text=api.GetUTF8Text() conf=api.MeanTextConf()
Could anyone explain the differences between these two snippets?
Overall Results of OCR Text Accuracy with 90% confidence intervals Google Cloud Platform's Vision OCR tool has the greatest text accuracy by 98.0% when the whole data set is tested.
While Tesseract is known as one of the most accurate free OCR engines available today, it has numerous limitations that dramatically affect its performance; its ability to correctly recognize characters in a scan or image.
Tesseract is very good on clean input text (like your example) if you tinker a bit. some suggestions:
I'll check back here to see if I can help more but do join the tesseract mailing list, they're really helpful.
Sidenote - I have some patches for pytesseract which I ought to publish for getting characters & confidences & words via the API (which wasn't possible a couple of months back). Shout if they might be useful.
The first example reads the file as a buffer and then relay it to tesseract-ocr without doing any modification while the second one reads file into opencv format which will then allow you to do some image touch up like changing the aspect ratio, gray scale and etc using the cv library. The second method is very useful if u want to do the image manipulation before passing the image to tesseract.
BTW, I am the owner of python-tesseract. If u want to ask question, you could always welcome to forward your question to http://code.google.com/p/python-tesseract
Joe
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