I am trying to detect and extract the "labels" and "dimensions" of a 2D technical drawing which is being saved as PDF using python. I came across a python library call "pytesseract" which has optical character recognition capability. I tried the demo on my image but it fails to detect most of the label/dimensions. Please suggest if there is other way to do it. Thank you**.
** Attached is a sample of the 2D technical drawing I try to detect
** what I am trying to achieve is to able to obtain the coordinate of every dimensions (the 160,120,10 4x45 etc) on the image, and extract the, as well.
About 16 months ago we asked ourselves the same question.
If you want to implement it yourself, I'd suggest the following process:
Extract the Canvas from the sheet
Separate the Cuts
Detect the Measure Regions on each Cut
Detect the individual attributes of the Measure Regions to understand where the Measure Start & End. In your particular example that's relatively easy.
Run the detected Measure Labels through OCR
Associate the Labels to the Measures
Verify your results
Alternatively you can also run it through our API and get the results as JSON.
Here's a quick visualization of the result:
Drawing Read (GT stands for General Tolerances)
Related
I am trying to detect the pdf417 barcode (2D barcode) from an image using python.
I will be receiving images of IDs where there is a barcode in them but it might not always be straight. So I am looking for an effective way to DETECT the pdf417 barcode using Python.
I tried all of the available methods that I could find (that uses python)
e.g.,
pdf417decoder: requires the image to be cut exactly around the barcode just like in the image below:
pyzbar: only detects 1D barcodes
python-zxing and zxing: didn't detect any of the pdf417 barcodes that I tried (around 10 different IDs - different country)
Barcode-detection: this is a DL approach that uses YOLO-V3 to detect barcodes, but again (after trying it), it only detects 1D barcodes...
Is there a method that I missed?
Am I using a wrong approach towards this problem?
Possible solution that I am thinking of: using computer vision (some filters and transformations) to detect a box that has black and white dots... Something similar to this.
Thanks!
After various trials, I ended up using an approach of template matching by OpenCV.
You need to precisely choose your template image that will be the search reference of your algorithm. You need to feed it some grayscaled images.
Then, you need to choose the boxes that have a result higher than a certain threshold (for me 0.55). Then apply NMS (non max suppression) to filter out the noisy boxes.
But keep in mind that there are many edge cases to encounter. If someone is interested to see the complete solution, please let me know.
Forgive me but I'm new in OpenCV.
I would like to delete the common background in 3 images, where there is a landscape and a man.
I tried some subtraction codes but I can't solve the problem.
I would like output each image only with the man and without landscape
Are there in OpenCV Algorithms what do this do? (then without any manual operation so no markers or other)
I tried this python code CV - Extract differences between two images
but not works because in my case i don't have an image with only background (without man).
I thinks that good solution should to Compare all the images and save those "points" that are the same at least in an image.
In this way I can extrapolate a background (which we call "Result.jpg") and finally analyze each image and cut those portions that are also present in "Result.jpg".
You say it's a good idea? Do you have other simplest ideas?
Without semantic segmentation, you can't do that.
Because all you can compute is where two images differ, and this does not give you the silhouette of the person, but an overlapping of two silhouettes. You'll never know the exact outline.
Attached below three images that I have processed already. The last part is to differentiate between the good samples and bad one
this two pictures are good samples
while the third one is not.
any idea how can I do image processing to solve this task.
i'm using OpenCV with python
Try counting the number of endpoints. Look at:
How to find endpoints of lines in OpenCV?
How can I find endpoints of binary skeleton image in OpenCV?
Detect holes, ends and beginnings of a line using openCV?
Explanation:
As you can see, once you have binarized and skeletonized (by the way, you should have 1px width lines so check the way you obtain the skeleton) the image you can see that the number of endpoints in the wrong one is 4 rather than in the other which should be 2.
Anyway you should attach the original pictures also, because maybe there is a better way to tackle the problem.
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.
I have an array made of 1 and 0 (image below), and I am working on a Python script that detects the borders of the central region (the big white blob) and marks all the internal points as 1. How would you do it?
I wrote a piece of code that does repeated connectivity search, but this doesn't seem the way to go - the region changes shape and new areas are added.
as I can't put a comment i put it here.
I had a problem close to yours: I wanted to select several holes and then calculate the area, the roundness...
What I did was to use the java implementation of python (jython) by which I could use a library called imageJ which is dedicated to image processing (all is include in Fiji). Navigating in the library is a bit fastidiuous but it is powerfull one
Here is the wand tool: http://rsbweb.nih.gov/ij/developer/api/ij/gui/Wand.html
Have a look here for "How getting pixels of a ROi" : http://fiji.sc/Introduction_into_Developing_Plugins#ImageJ.27s_API