Thesis (PDF) - Signal & Image Processing Lab

6.4. APPLICATION EXAMPLES 99
6.4 Application examples
A common usage of opening and opening by reconstruction operators is cleaning noisy
images as an intermediate step in various applications. Our operators, defined on
extrema watershed tree, have an important feature: duality. In the next paragraphs
we will show a number of possible applications that may use the EWT-based filtering
as an intermediate step. The presented applications are OCR (Optical Character
Recognition) algorithms and dust and scratch removal from images.
6.4.1 Pre-processing for car license plate number recogni-
tion
Morphological operators are a common choice for applications that require image
simplification. One kind of such an application is pre-processing for OCR (Optical
Character Recognition) algorithms. We have chosen a specific OCR algorithm, used
for recognition of license plate numbers, that was developed in [25]. This algorithm
uses a mask for each digit and looks for the best correlation among these masks with
an image. Any noise that exists in the image degrades the correlation value and
interferes with the recognition.
This particular algorithm has an important benefit: it calculates a coefficient of
recognition quality. This coefficient enables us to measure recognition improvements,
when using different pre-processing algorithms. The exact definition of this coefficient
is presented in [25].
Consider an example license plate shown in Fig. 6.29. This image is corrupted with
different kinds of noise, see Fig. 6.30. The correlation is done on the gray scale image
shown in Fig. 6.31. It is difficult to automatically recognize the numbers without pre-
processing, because of the noise level. We have done opening by reconstruction, using
the structuring element shown in Fig. 6.28. The diameter of this SE is equal to the
width of digits lines. This size of SE assures that we filter as much noise as possible
without damage to the numbers. The resulting image can be seen in Fig. 6.32. In
this figure, almost all the noise is gone, and the image is much clearer and suitable
for OCR. The effect of the proposed pre-processing can be seen in table 6.1. Without
the pre-processing, the OCR failed to recognize correctly the plate number. The pre-
processing, not only removes the noise, but also simplifies the image. Therefore, when