Abstract:

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

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