Image Processing Softwares - image-processing

I need to process the text inside the image. The image is of the meter reading the data.
I tried with NewOcr and few other free online image processing software, but they didnt read the data correctly.
Which are the best image processing softwares(both freeware and shareware) that can read meter data accurately?
PS: I need to get the exact reading from the image processed. So, I cannot afford to handle inappropriate data.
The images which will be processed can be found on this link.

Tesseract perhaps? http://code.google.com/p/tesseract-ocr/ im not sure you will be able to avoid inappropriate data, the OCR will make a mistake eventually and some will slip through.

I suggest you tu use matlab ocr for text recognition. In general for image processing i sugges you image processing MATLAB toolbox

Related

Tesseract for License Plate (especially Korean version)

I'm working on my project for License Plate Recognition using OpenCV & Tesseract.
I use OpenCV to change original image to processed image so that Tesseract can read it well.
For example)
Original Image
Processed Image
But the result shows "38다9502"and it recognized 3 to 5.
These situation happens frequently especially when the number is 3 or 5.
Is there any suggestion or solution for it??
You can try retraining tesseract with some of your own data. It looks like a good candidate for simply fine-tuning the model. You may not even need much data, just give it several examples of the digits it is having trouble with.
Instructions for retraining are here: https://github.com/tesseract-ocr/tesseract/wiki/TrainingTesseract-4.00
1)First it can be done with few image processing techniques which is mentioned in this link(https://cvisiondemy.com/license-plate-detection-with-opencv-and-python/)
2)Next if it doesn't show any improvement you can try image thresholding which you can go through in this link(https://docs.opencv.org/master/d7/d4d/tutorial_py_thresholding.html)
3)If above steps didn't work ,then try to enlarge your image size.
I solved this question with using multiple models supported by Tesseract.
With Hangul model, I only received accurate information of Hangul word, not Numbers.
However, with English model, I can received accurate information of Numbers.
So I used these models in parallel and it resulted 99% accuracy of LPR.

How to do segmentation based on some filters(e.g. TRAFFIC SIGNALS) from live streaming data

I am supposed to do traffic symbols recognition from live streaming data. Please tell me how to automate the process of segmentation. I am able to recognize the symbols using Neural Networks from segmented data but stuck in the segmentation part.
I have tried it using YOLO, but I think I am lacking something.
I have also tried it with openCV.
please help
INPUT IMAGE FRAME FROM LIVE STREAM
OUTPUT
I would suggest you follow this link:
https://github.com/AlexeyAB/darknet/tree/47c7af1cea5bbdedf1184963355e6418cb8b1b4f#how-to-train-pascal-voc-data
It's very simple to follow. You basicly need to do 2 steps. Installing and creating the data you want to use (road signs in your case).
So follow the installation guide and then try to find a dataset of road signs, use your own or create your own data set. You will need the annotation files as well (you can generate them yourself easily if you use your own dataset(s) - this is explained in the link as well). You don't need a huge amount of pictures, because darknet will augment the images automaticly (just resizing though). If you use a pretrained version you should get "ok" results pretty fast ~after 500 iterations.

Image pre-processing in OCR

Our project is all about OCR and base on my research, before performing the character recognition it will go through on pre-processing stage. I know we can use openCV for that but we can't use it base on our rules.
My question is, can someone tells me the step-by-step of pre processing and the best method/algorithm to use.
like what I know,
1.YUVluminace
2.greyscale
3.otsu thresholding
4.Binarization
5.Hough transform
Original Image> YUVluminace> greyscale what's next??
thanks!
In some of my older blog posts, I addressed some parts of your questions:
Binarization on various image qualities from mobile cameras:
http://www.ocr-it.com/guide-to-better-mobile-images-from-cell-phone-camera-for-higher-quality-ocr
Image pre-rpocessing and segmentation for better OCR:
http://www.ocr-it.com/user-scenario-process-digital-camera-pictures-and-ocr-to-extract-specific-numbers
In reality, there is no step-by-step, per my experience. You could use original image for OCR if you wanted to, with means no pre-processing is nessesary. Yes, pre-processing will help, but it depends on the source and type of your images (which you did not specify). For example, a typical office document scanned on a professional scanner with Kofax VRS requires no pre-processing before OCR. Mobile camera image requires a lot of pre-processing. Picture from a parking garage camera will require a lot of pre-processing, but different steps and algorithms from mobile camera picture.
I think decide what is the next major limiting factor in your images, pre-process against it, then look for the next correctable issue.

image preprocessing tesseract

I'm doing a business card scanner for my final examination about digital image processing, and I would ask to you how I have to preprocess a photo of a business card so tesseract can recognize text. I tried a lot of things, like erosion, dilation, thresholding, but I can't have a good result... Can you help me?
Thank you
Marco
If your concern is only about text recognition and not about preprocessing, consider using ScanTailor. It is an excellent pre-processing tool and it is open source.
If you want to implement the pre-processing yourself, you might want to have a look at this paper - especially the skew correction and the background estimation.The results of the algorithms described here are good. ScanTailor uses some of these.
I would recommend the open source C++ image processing library OpenCV combination with the open source free Optical Character Recognition (OCR) library tesseract.
Since your information of your problem isn't quite specific, i can answer your question in general
The main procedure in OCR is:
perform some kind of preprocessing on the image
text detection to get your ROI (Region of interest, the region containing your text)
character detection (take the text-only image and use it as input for tesseract
a few words about tesseract:
There is a lot of information to the library available online. It is a google open source library used for the google books OCR purpose. Can also handle layout analyzes in your image, but isn't perfect in this, therefore a preprocessing yourself and using tesseract only for the real character recognition part can lead to a better result. Feel free to question, if you still have questions, or if I missunderstood your question.

Image processing language/environment

I am interested in studying some image processing. I imagine matlab is the best way to go about that but right now I don't have access to matlab. I tried octave but for some reason it can't even load a png, bmp or anything other than 1 specific format. R doesn't seem to be the key here either.
What is the language of choice here? Perl?
Also can anyone point me to any other good tutorials that I may have missed on image processing?
Opencv is an excellent image processing library. Although written in C it comes with some high level tools to display images handle image files, mouse events etc so you can experiment without writing a lot of windows code.
It also works with python, although I haven't used it with the PIL.
If you are interested in how the algorithms work then implementing them yourself using python and numpy for the matrix ops is easy.
I guess it depends on what you want to do. Matlab certainly is a high end choice, but for a lot of things the image modules of general purpose programming languages do the trick.
I did some pixel mangling and image processing with PIL, the python image library. It is perfectly sufficient for processing single RGB images of reasonable size (say, what a consumer digital camera delivers). It can handle alpha channels, has some filters, more or less quick methods of accessing the pixel information - and it is python, a very straightforward and readable language.
The recommended language in my computer vision class was Ch with the OpenCV library. Ch is basically an interpreted version of C, the syntax is quite similar but has a few nice features, like treating arrays as matrices. OpenCV will house pretty much any image processing function you could need.
I think any free programming environments will do basic image processing well. If speed is not an issue, Processing will work fine and you can easily extend your code to Java in the future.
Have a look at Adobe Pixel Bender. It's really fun to play with.

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