I need to extract digital digits from the digital weighing scale. OCR and Pytesseract libraries are not working for this problem statement. Is there any better libraries or solution?
Below is the input image from which I'm trying to extract the digits.
Input image
Perhaps this article is what you're looking for: https://pyimagesearch.com/2017/02/13/recognizing-digits-with-opencv-and-python/
The article demonstrates the use of OpenCV to read LCD-digits off of tiny displays.
Related
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!
I got this image of a simple math expression Tesseract fails to read:
I've tested a screenshot of the same expression written on an Android phone and it was read pretty well. So I thought it's a font problem.
I considered:
Preprocess the image by inverting or removing the red areas
Training Tesseract with images (StackOverflow question with no answers)
Using WhatFontIs.com to find similar font then training Tesseract with the font file with TrainYourTesseract
But as I was typing the question, I looked around for more.
And this answer prompted me to double check my sanity with this VietOCR software which outputs 8-3, close enough!
Then I messed around the software and found that I could pass --psm 7 (Page Segmentation Mode 7: Treat the image as a single text line) to my script, which works well for my math expressions:
pytesseract.image_to_string(img, config='--psm 7')
List of PSMs
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.
I am new to openCV and python both. I am trying to count people in an image. The image is supposed to be captured with an overhead camera or the way a CCTV camera is placed.
I have converted the colored image into binary image and then inverted the binary image. Then I used bitwise OR on original and inverted binary image so that the background is white and the people are colored.
How to count these people? Is it necessary to use a classifier or can i just count the contours ,if yes then how to count them?
Plus there are some issues with the technique I'm using.
Faces of people are light in color so sometimes only hair are getting extracted.
The dark objects other than people also get extracted.
If the floor is dark it won't give the binary image that is needed.
So is there any other method to achieve what I'm trying to do here?
Not sure but it may worth to check there.
It explain how to perform face recognition using openCV and python in pictures and extand it to webcam here, it's not quite what your looking for but may give you some clue/
I am new to the image processing subject. I'm using opencv library for image processing with python. I need to extract symbols and texts related to those symbols for further work. I saw some of developers have done handwritten text recognitions with Neural network, KNN and other techniques.
My question is what is the best way to extract these symbols and handwritten texts related to them?
Example diagram:
Details I need to extract:
No of Circles in the diagram.
What are the texts inside them.
What are the words within square brackets.
Are they connected with arrows or not.
Of course, there is a method called SWT - Stokes Width Transform.
Please see this paper, if you search it by its name, you can find the codes that some students have written during their school project.
By using this method, text recognitions can be applied. But it is not a days job.
Site: Detecting Text in Natural Scenes with
Stroke Width Transform
Hope that it helps.
For handwritten text recognition, try using TensorFlow. Their website has a simple example for digit recognition (with training data). You can use it to implement your own application for recognizing handwritten alphabets as well. (You'll need to get training data for this though; I used a training data set provided by NIST.)
If you are using OpenCV with python, Hough transform can detect circles in images. You might miss some hand drawn circles, but there are ways to detect ovals and other closed shapes.
For handwritten character recognition, there are lots of libraries available.
Since you are now to this area, I strongly recommend LearnOpenCV and and PyImageSearch to help you familiarize with the algorithms that are available for this kind of tasks.