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We want to train a particular font and all the alphabets from A-Z and all numbers from 0-9. How many positive and negative samples of each would do the job? It would be a tedious task to do though but tesseract is not that accurate to read number plates of moving vehicles. Any other suggestions to do the task?

tdelaney
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Rajat Kalyan
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I am quoting from the following Wikipedia article- https://en.m.wikipedia.org/wiki/Automatic_number_plate_recognition

There are seven primary algorithms that the software requires for identifying a license plate:

1.Plate localization – responsible for finding and isolating the plate on the picture.

2.Plate orientation and sizing – compensates for the skew of the plate and adjusts the dimensions to the required size.

3.Normalization – adjusts the brightness and contrast of the image.

4.Character segmentation – finds the individual characters on the plates.

5.Optical character recognition.

6.Syntactical/Geometrical analysis – check characters and positions against country-specific rules.

7.The averaging of the recognised value over multiple fields/images to produce a more reliable or confident result. Especially since any single image may contain a reflected light flare, be partially obscured or other temporary effect.

Coming back to your question Haar cascades can be used to localise number plates. However for the OCR part I would personally recommend a CNN network. You can find an implementation here- https://matthewearl.github.io/2016/05/06/cnn-anpr/

There is also this library specialised in the task-https://github.com/openalpr/openalpr check out that as well

For haar cascade-https://github.com/opencv/opencv/blob/master/data/haarcascades/haarcascade_licence_plate_rus_16stages.xml

Good luck

Amal Vincent
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