Develop Reference

"Newbie" questions about PyTesseract OCR

I'm working on an application that would extract information from invoices that the user takes a picture of with his phone (using flask and pytesseract).
Everything works on the extraction and classification side for my needs, using the image_to_data method of pytesseract.
But the problem is on the pre-processing side.
I refine the image with greyscale filters, binarization, dilation, etc.
But sometimes the user will take a picture that has a specific angle, like this:
invoice
And then tesseract will return characters that don't make sense, or sometimes it will just return nothing.
At the moment I "scan" the image during pre-processing (I'm largely inspired by this tutorial: https://www.pyimagesearch.com/2014/09/01/build-kick-ass-mobile-document-scanner-just-5-minutes/), but it's not efficient at all.
Does anyone know a way to make it easier for tesseract to work on these type of images?
If not, should I focus on making this pre-processing scan thing?
Thank you for your help!

OCR / ICR for handwritting and logos

I have to do the following task on Python and I have not idea where
to begin:
OCR of handwritten dates
Page/document orientation detection for pretreatment
Stamp and Logo Detection and classification
a. Orientation variation included
b. Quality degradation to be considered
c. Overlying Primary Content
Anybody could help me?
THANKS IN ADVANCE¡¡

You can ocrmypdf to extract text from a pdf. It will extract text from the page and return a pdf same like original pdf with text on it. For detection of logos, you need to implement a computer vision-based model. if you need more details then please specify your requirement in details

Adding text search to content based image retrieval (convnet)

I've implemented CBIR app by using standard ConvNet approach:
Use Transfer Learning to extract features from the data set of images
Cluster extracted features via knn
Given search image, extract its features
Give top 10 images that are close to the image in hand in knn network
I am getting good results, but I want to further improve them by adding text search as well. For instance, when my image is the steering wheel of the car, the close results will be any circular objects that resemble a steering wheel for instance bike wheel. What would be the best possible way to input text say "car part" to produce only steering wheels similar to the search image.
I am unable to find a good way to combine ConvNet with text search model to construct improved knn network.
My other idea is to use ElasticSearch in order to do text search, something that ElasticSearch is good at. For instance, I would do a CBIR search described previously and out of the return results, I can look up their description and then use ElasticSearch on the subset of the hits to produce the results. Maybe tag images with classes and allow user to de/select groups of images of interest.
I don't want to do text search before image search as some of the images are poorly described so text search would miss them.
Any thoughts or ideas will be appreciated!

I have not found the original paper, but maybe you might find it interesting: https://www.slideshare.net/xavigiro/multimodal-deep-learning-d4l4-deep-learning-for-speech-and-language-upc-2017
It is about looking for a vector space where both images and text are (multimodal embedding). In this way you can find text similar to a images, images referring to a text, or use the tuple text / image to find similar images.
I think maybe this idea is an interesting point to start from.

I want to extract table information from OCR Data

I want to extract table information from OCR data, I have raw text and it's text.
I tried pytesseract but couldn't find the actual Implementation.
Here is an image: https://drive.google.com/open?id=1CGJwbmf5snoXvwlQAsRAxIRRixbT_Q8l
I tried this: https://github.com/WZBSocialScienceCenter/pdftabextract
this method didn't work for me at all.
I want a tabular structure of this table from OCR data for my further processing.

pdftabextract is not an OCR. It requires scanned pages with OCR
information, i.e. a "sandwich PDF" that contains both the scanned
images and the recognized text. You need software like tesseract or
ABBYY Finereader for OCR.
Please try tesseract it has a relatively easier implementation.

Extract text from light text on withe background image

I have an image like the following:
and I would want to extract the text from it, that should be ws35, I've tried with pytesseract library using the method :
pytesseract.image_to_string(Image.open(path))
but it returns nothing... Am I doing something wrong? How can I get back the text using the OCR ? Do I need to apply some filter on it ?

You can try the following approach:
Binarize the image with a method of your choice (Thresholding with 127 seems to be sufficient in this case)
Use a minimum filter to connect the lose dots to form characters. Thereby, a filter with r=4 seems to work quite good:
If necessary the result can be further improved via application of a median blur (r=4):
Because i personally do not use tesseract i am not able to try this picture, but online ocr tools seem to be able to identify the sequence correctly (especially if you use the blurred version).

Similar to #SilverMonkey's suggestion: Gaussian blur followed by Otsu thresholding.

The problem is that this picture is low quality and very noisy!
even proffesional and enterprisal programs are struggling with this
you have most likely seen a capatcha before and the reason for those is because its sent back to a database with your answer and the image and then used to train computers to read images like these.
short answer is: pytesseract cant read the text inside this image and most likely no module or proffesional programs can read it either.

You may need apply some image processing/enhancement on it. Look at this post read suggestions and try to apply.