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I have a series of images that contain a single word between 3-10 characters. They are images created on a computer itself, so quality of the images is consistent and the images don't have any noise on them. The fonts are quite large (about 30 pixels in height). This should already be easy enough for tesseract to read accurately but what are some techniques I can use for improving the speed, even if it's only an improvement of a few milliseconds?

The character set consists of uppercase letters only. As the OCR task in this case is very specific, Would it help if I train the tesseract engine with this specific font and font-size or is that overkill?

Edited to include sample

Other than tesseract, are there any other solutions that I can use with C/C++ that can provide better performance? Could it be done faster with OpenCV? Compatibility with Linux is preferred.

Sample

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If all the letters have same size & style, you can try something really simple like running blob detection followed by template matching of individual letters. I am not sure how will it compare with tesseract but it is a very simple experiment. (Additionaly, lowering the resolution will speed things up...) You can also have a look at this question: Simple Digit Recognition OCR in OpenCV-Python, it may be relevant

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