Research Information System
Journal Of The Faculty Of Engineering And Architecture Of Gazi University, vol.37, no.2, pp.843-856, 2022 (SCI-Expanded)
Character recognition in natural images is a very difficult problem due
to many factors such as variability of light, background clutter, severe
blur, inconsistent resolution and different scale depth. In addition to
these features, distortions in characters and numbers are encountered
in street view photographs with the effect of natural events. Detecting
and reading house numbers from street views is a computer vision problem
that falls under the category of natural scene text recognition.
Convolutional neural network (CNN) model is one of the most commonly
used deep learning (DL) methods in image analysis. In this study,
firstly, CNN based DL method was applied to read characters from
pictures that contain house numbers in their natural image. However,
successful results could not be obtained, especially in cases where
there are more than one house number in the image or when the depths are
very variable. A new approach DDL (deep in deep learning) using two
different CNN models was proposed to increase the accuracy of the DL
method and also to reduce the data size created by natural images. The
performance of the proposed DDL approach was compared with the
performance of the DL approach using real data consisting of 17,618
images with 113 GB (gigabyte) size consisting of building street images
with GPS location information taken from 35 neighborhoods of Kayseri
Metropolitan Municipality (KBB) Yeşilhisar district for 2019.
Experimental results showed that the proposed DDL approach produced more
accurate results and used less storage space than DL approach