Abstract:
The paper proposes a method for recognizing the content
of scanned images of poor quality using convolutional neural networks (CNNs).
The method involves the implementation of three main stages.
At the first stage, image preprocessing is implemented, which consists
of identifying the contours of its alphabetic and numeric elements and basic
punctuation marks.
At the second stage, the content of the image fragments inside the identified
contours is sequentially fed to the input of the CNN, which implements a multiclass
classification.
At the third and final stage, the post-processing of the set of SNA responses
and the formation of a text document with recognition results are implemented.
An experimental study of all stages was carried out in Python using the Keras
deep learning libraries and OpenCV computer vision and showed fairly good
results for the main types of deterioration in the quality of a scanned image:
geometric distortions, blurring of borders, the appearance of extra lines and spots
during scanning, etc.
Key words and phrases:image processing, convolutional neural network, Python, Keras, OpenCV.
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