My question is about any help and ideas about to how to localize the characters at that stage, after Otsu thresholding? I do know the width and height of each character (up to a small uncertainty caused by hand crafted measurements) and I also know that they always constitute two lines with 7 elements in each.

Maybe some additional dilation/erosion operations on the binary image would help to reduce non-character clutter, but certainly I would not be able to completely eradicate them. Now I am almost able to localize each character, but as you can see in the second image, due to bad lighting conditions, some noisy clutter is passed as foreground (especially around 0 and F, on the left side). From the above operation, I have the following result, with the optimal blur kernel size: If I would be able localize each character almost perfectly, I could run a classifier just containing its bounding box and I won't need a lot of negative transition/background etc. The first thing I tried is to convert the image into grayscale, downscale it and run a low pass filter to remove the high frequency noise and apply Otsu Thresholding. Since I am able to localize the serial numbers into a rectangle which only includes a solid background except the characters, I thought of a simple foreground/background segmentation scheme. This brute force approach does not seem to be very efficient for me, since 1) a lot feature extraction + classification operations have to run for each window 2) I have to manually label a lot of background (negative) samples, which include transition areas between two characters, the vertical space between two lines, pure background etc. The basic thing to do is, since I know the aspect ratio and relative sizes of the characters, training a simple classifier (HOG + Linear SVM) on labeled character and background images (I have to do this in anyway) and then run it via a classical sliding window fashion, then apply non-maximum suppresion to remove false positive detections. Then I quickly turned to custom solutions.

Unfortunately, either I failed to adjust the API properly or this particular font is not in Tesseract's training set, since Tesseract is not able to properly parse the serial number. I have first tried the Tesseract API after localizing the numbers into such tight images. Now my aim is to read these serial numbers.

These serial numbers are located on the images of various products I am able to localize these by using a lot of image processing and geometry transformation algorithms into the following form:

I have the following problem: I have some serial numbers which always consist of 2 lines of 7 characters, (0-9 and A-Z), with a total of 14 characters.

Serial images