Projects

An OCR solution to detect label position and volume of liquid in the bottle

TECHNOLOGIES NumPy, OpenCV, Python
AREAS OF EXPERTISE Computer vision, image processing
TEAM 1 developer

A company producing bottled products had a task to provide the quality control of its products. To achieve that, it was necessary to monitor the level of liquid in the bottle and the position of the pasted label. If the level of liquid and the position of the label do not correspond to the admissible values, the product is of poor quality.

Challenges
  1. Recognize and single out the object of interest (bottle)
  2. Determine the position of the label on the bottle
  3. Determine the level of liquid in the bottle
  4. Compare the obtained values of the position of the label and the level of liquid with the admissible ones
Solutions
  1. To single out the object of interest, we first determined the background of the image obtained from the webcam. To achieve this, we used the OpenCV function, which allows figuring out the corresponding object based on the typical section, and besides removed the “noise” outside the object.
  2. To solve the problem of determining the position of the label, the image is adjusted to HSV format. Then, using OpenCV, blurring of the image and "noise" reduction, the boundaries of the label are determined.
  3. To determine the liquid level, the image was also adjusted to HSV format and “noise” was reduced.
  4. The admissible values for the position of the label and the level of liquid are entered into the system in advance, taking into account the existing standards. The system automatically compares the obtained values with the admissible ones, based on which the conclusion about the quality of the product can be made.
Results

As a result, highly accurately determining the level of liquid in the bottle and the place where the label was pasted became possible, the data obtained were compared with regular results.

Comparison of the obtained values with the admissible ones allows detecting products of poor quality.

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