Hot answers tagged

9

What you really require is probably some morphological operation like dilation followed by erosion. This is called as Closing operation. May be in your case- just dilation itself might be good. There was a similar question asked previously - which can help with other aspects. monochrome (1-bit black-and-white) image conversion How do I reconstruct text ...


8

Here is what I did for a client (What you are asking is the same). Assuming that you have access to certain type of a pattern on the image (or the center of the hole), you could always detect the template to obtain the location of a possible unwarp: Note that in the transformed image, two region of interests are defined and the region within which we would ...


7

As far as i know there are no native opensource Java OCR SDKs. There are Java APIs which wrap calls for native interfaces, for example, for one of the most popular opensource OCR engines - Tesseract (http://groups.google.com/group/tesseract-ocr/) - there are some Java wrappers like tesjeract (http://code.google.com/p/tesjeract/) or Tess4J (http://tess4j.sf....


6

Here are a few ideas: First convert from color to grayscale. It looks like you have fairly good contrast already. There are various methods to perform this conversion; choose the simplest at first: gray = (red + green + blue)/3. Quite often you don't need anything better than that. For some applications, using just the green color plane is sufficient. If ...


5

Do not struggle forming a database of images to match via descriptors. This would be too computationally cumbersome and would require immerse amount of training. Such a scalable solution doesn't exist out of the box yet. I would rather rely on Neural Networks or SVMs to train the possible appearances of characters. Of course using a classifier relies on ...


5

First, you will need an image processing algorithm to detect local features of the digital images you have. 1 and 2 provide good reviews of such algorithms.: After extracting the local features, a matching process between the extracted features and your set of labels is used to link the labels with acquired images. The second references goes over few ...


4

I think the open-source command-line program potrace, might do what you want. It converts bitmaps to bezier curves and has a bunch of options allowing you to trade off smoothness and accuracy. The open-source Inkscape vector (svg) editor has potrace built in (under the Path->Trace Bitmap menu option.) The result of applying your example in Inkscape, (I ...


3

It is a bit hard to understand what you are trying to do. What are these signs? The one you posted looks like a wheel. Are there meaningful categories that you can name? If so, then this is a supervised learning (classification) problem, and you should use a classification algorithm such as SVM. If there are no clear labels, but you want to group together ...


2

you can remove this using a low-pass filter. that's either done in frequency space, or just take the (difference of) gaussian of the image.


2

As said in the comments an efficient way is to first detect letters, words and text with OCR. Then try to expand each text zone to its corresponding text bubble. Depending on the text bubble design there are different approaches. However, a solution that could work well and be robust would be to perform edge detection on the near surrounding of the ...


2

Check out OpenALPR (http://www.openalpr.com). It uses Local Binary Patterns (LBP) to identify plate regions -- so it should work independently of background colors.


1

The shapes you are interested in are very regular while the noise you are trying to deal with will be less regular. Ideally, you will only have two shapes at the end of your processing: wide rectangles for the horizontal parts of the display and tall rectangles for the vertical parts. You can leverage this fact in how you shape your structuring element when ...


1

Here is a possible solution: Use FFT2 to find angle of text and approximate spaces between lines Rotate the image to be at 0 angle Sum the image column-wise Find threshold that splits lines of text from non text lines Show lines on rotated image. It can be improved by finding a more complex curve (rather than line). Here are the results: Original After ...


1

Aha, this is a quite funny story that I've heard about CV. Are you a bio guy? Any way, here are my suggestions. If you are a bio guy and just want to finish this project ( I mean successfully identify each insect in a video frame ), go for barcode, QR etc. They are labels though their contents are not directly readable by eyes. However, you will have a ...


1

I believe this question is off-topic, but this page suggests that the algorithm is described in:


1

Yes, if applied properly, HOG is a good feature extractor even for OCR. I could point you towards an OpenCV sample and a MATLAB digit classification tutorial which do exactly that. However, if you like to get state-of-the-art performance, I would suggest deep learning methods, more specifically convolutional neural networks. Many works demonstrated the power ...


1

A short answer to your question is "NO". To my best knowledge, there is no such a smart end-to-end OCR system that can handle various OCR tasks. However, there are OCR products could reasonably handle some tasks under controlled environments. You seem to be confused of different OCR related tasks and you erroneously believe these tasks are quite similar ( ...


1

This problem is called (ink) bleeding. A popular approach is to separate or segment the document image using graphical models. Relevant papers include: Document Ink Bleed-Through Removal with Two Hidden Markov Random Fields and a Single Observation Field User-Assisted Ink-Bleed Reduction Bleed-through removal in degraded documents Directed Assistance for ...


1

The main issue here is the directional lighting which illuminates mainly one side of the characters, so that a part of them disappears in the background. If you rotate the part, the appearance of the letters will change; this is bad for recognition. You may also face fragmentation of the characters (K for example). I don't like this adaptive thresholding at ...


1

It looks like using the polartocartesian transformation is destroying the characters (look at the "M", "E" before and after...). I think it will be a better idea to think about some rotations mechanism using imrotate. Any way, I'd try the following additional pre-proc: 1. you might want to perform some morphological operations on the image after the ...


1

I think you could specify a higher DPI for higher 'resolution' (which effectively changes the pixel number of your image) in loading the data. Resizing the data will usually cause small changes to your data that are usually irreversible (unless you know exactly the correct parameters for resizing).


1

why use OCR techniques that are sensitive to vanishing points? projective distortion just skews the letters- right? So, why not just model those expected skews into the OCR templates for each letter? Also, the nearby letters would skew similarly, so you could probably use correlated parallel lines among neighboring letters to estimate the amount of ...


1

Probably the simplest OCR algorithm would be the case where the characters are from a known font, in perfect rows with no distortion or rotation, and then you can find occurrences of each character in the text by doing cross-correlation with the known characters from the known font. Or even simpler if you can create the font before it's used, with cheaper ...


Only top voted, non community-wiki answers of a minimum length are eligible