I'm using the latest version of Tesseract (5.0), and I'm trying to determine whether or not I can insert some preprocessing steps that will -not- affect the form of the final image.
For example, I might start out with an image such
as this.
There are different levels of shadow/brightness, so I might use adaptive Gaussian thresholding to avoid shadows during binarization.
I will now run this through tesseract, with the hope of creating an OCR'd PDF in the end. However, I want the image that the end user (and I) see to be the full-color, original image, with the text from the transformed image underlaid
Is there a way to manage this? Or am I completely missing the point here.
I was provided an answer on another forum, and wanted to share it here.
Instead of using the built in PDF option in Tesseract, I used the hOCR setting. My pipeline went:
Preprocess image (thresholding, etc)
Run tesseract with the following command: tesseract example1.jpg example1 -l eng hocr
Use the hocr-pdf module from Ocropus to merge the hocr'd material with the ORIGINAL IMAGE, no preprocessing.
Related
I am working on a project where my task is to identify machine part by its part number written on label attached to it or engraved on its surface. One such example of label and engraved part is shown in below figures.
My task is to recognise 9 or 10 alphanumerical number (03C 997 032 D in 1st image and 357 955 531 in 2nd image). This seems to be easy task however I am facing problem in distinguishing between useful information in the image and rest of the part i.e. there are many other numbers and characters in both image and I want to focus on only mentioned numbers. I tried many things but no success as of now. Does anyone know the image pre processing methods or any ML/DL model which I should apply to get desired result?
Thanks in advance!
JD
You can use OCR to the get all characters from the image and then use regular expressions to extract the desired patterns.
You can use OCR method, like Tesseract.
Maybe, you want to clean the images before running the text-recognition system, by performing some filtering to remove noise / remove extra information, such as:
Convert to gray scale (colors are not relevant, aren't them?)
Crop to region of interest
Canny Filter
A good start can be one of this tutorial:
OpenCV OCR with Tesseract (Python API)
Recognizing text/number with OpenCV (C++ API)
I have an image of a layer of carbon nanotubes taken with an electronic microscope and I would like to extract the "shape" of every nanotube.
I think that ImageJ could be very helpful since it has already led me to encouraging results, but I am sure that they could be improved.
Here is the image I start with :
I have seen that there are many tools to detect for example cells, or to divide an image in two areas (with the trainable Weka segmentation tool for instance), but I did not find anything to "follow" a tube from the beginning to the end. Does such a tool exist ?
What would you advise me to do to clean the image ? So far, I have tried 'auto local threshold' (loss of information), 'Remove outliers', 'analyse particles' (to remove the too-little-to-be-tubes-particles), and some skeleton tools + OrientationJ to extract information.
Thank you very much for your help !
EDIT : the "Tubeness" plugin was very helpful Tubeness documentation
I'm trying to convert a tangent space normal map to a height/displacement map. For sure this will be not 100% accurate speaking in terms of "exact height" for each pixel. But the relative height from each pixel to the next is more than enough.
Available Algorithm + Info's:
http://www.cournia.com/devnull/n2h/n2h.pdf
Questions:
1. How to convert a normal-to-height map in Photoshop/Gimp? Is there a way using these tools? Beside; I don't wan't to use CrazyBump or any other Texture-Tools. This has to run via CL later on. A Photoshop solution is more or less just a pre-step to understand workflow a bit better.
If not possible with PS/Gimp; how to include the algorithm in an imagemagick process?
I've checked already Doom3:-Normal2Height; Crazybump & all other texture tools like Nvidia's PS-Plugin, xNormal, Awesomebump, SSBump, etc. I'd need this function working with Imagemagick.
Any help is very much welcome. Python preferable.
thx
There are a couple of possibilities for doing that with ImageMagick.
Firstly, you could implement your own process module. When running configure to install ImageMagick, you would then do:
./configure --with-modules=yes
Then, when you want to apply your bumpmap processing on the command-line, you would do:
convert input.png -process analyse <param1> <param2> result.png
Your processing needs to be written in C/C++ and the best description I know of is on Alan Gibson's webpages here.
Secondly, you could write your entire processing using Magick++ which is the C++ binding to ImageMagick. Best description I know of is here with sample code here.
I know this has been posted elsewhere and that this is no means a difficult problem but I'm very new to writing macros in FIJI and am having a hard time even understanding the solutions described in various online resources.
I have a series of images all in the same folder and want to apply the same operations to them all and save the resultant excel files and images in an output folder. Specifically, I'd like to open, smooth the image, do a Max intensity Z projection, then threshold the images to the same relative value.
This thresholding is one step causing a problem. By relative value I mean that I would like to set the threshold so that the same % of the intensity histogram is included. Currently, in FIJI if you go to image>adjust>threshold you can move the sliders such that a certain percentage of the image is thresholded and it will display that value for you in the open window. In my case 98% is what I am trying to achieve, eg thresholding all but the top 2% of the data.
Once the threshold is applied to the MIP, I convert it to binary and do particle analysis and save the results (summary table, results, image overlay.
My approach has been to try and automate all the steps/ do batch processing but I have been having a hard time adapting what I have written to work based on instructions found online. Instead I've been just opening every image in the directory one by one and applying the macro that I wrote, then saving the results manually. Obviously this is a tedious approach so any help would be much appreciated!
What I have been using for my simple macro:
run("Smooth", "stack");
run("Z Project...", "projection=[Max Intensity]");
setAutoThreshold("Default");
//run("Threshold...");
run("Convert to Mask");
run("Make Binary");
run("Analyze Particles...", " show=[Overlay Masks] display exclude clear include summarize in_situ");
You can use the Process ▶ Batch ▶ Macro... command for this.
For further details, see the Batch Processing page of the ImageJ wiki.
Apologies for tagging this just ImageJ - it's a problem regarding MicroManager, a microscopy plugin for it and I thought this would be best.
I'd recently taken images for an important experiment using MicroManager (a recent version, though I cannot recall the exact number). The IT services at my institution have recently been having some networking problems and my saved preferences for the software had been erased. I'd got half way through my experiment when I realised that I'd saved my images as separate image files (three greyscale TIFFs plus metadata text files) instead of OME-TIFF iamge stacks.
All of my ImageJ macros for image processing rely on having a multiple channel image stack, so this is a bit of a problem. Is there any easy way in MicroManager (or ImageJ) to bulk convert these single channel greyscale images into the OME-TIFF image stack after the images have already been taken?
Cheers.
You can start with a macro like this one:
// Convert your images to a stack
run("Images to Stack", "name=Stack title=[] use");
// The stack will default the images to time points. Convert to channels
run("Stack to Hyperstack...", "order=xyczt(default) channels=3 slices=1 frames=1 display=Color");
// Export as OME-TIFF
run("Bio-Formats Exporter");
This is designed to reconstruct one dataset at a time (open 3 images, run the macro and export the OME-TIFF).
If you don't want any dialogs to show you can pass an output directory to the Bio-Formats exporter:
run("Bio-Formats Exporter", "save=/path/to/image.ome.tif export compression=Uncompressed");
For the output file name you can get the original image name in the macro with getTitle()
There is also a template example on iterating over all the files in a directory, if you want to completely automate the macro. However this may take some tweaking since you want to operate on your images 3 at a time.
Hope that helps!