In the proposed approach, a deep learning method detects a street sign object in the EXIF. The performance of the proposed approach has been examined on the natural EXIF data sets obtained from the Kayseri Metropolitan Municipality. In this study, a new hybrid approach, ODPR, which utilizes object detection (O), distance estimation (D), rotation (R) and projection (P) methods, is proposed to detect street sign objects in EXIF with their locations. Transferring the objects in EXIF data sets with absolute coordinates on the earth significantly contributes to GIS. On the other hand, exchangeable image file (EXIF) format is a special file format that contains camera direction, date-time information and GPS location provided by a digital camera that captures the images. Thus, it is essential to collect and update these data. The most important raw material in GIS is spatial data. Geographical information systems (GIS) are the systems where spatial data are stored and analyzed. Our results show promising potentials for these algorithms to be deployed in the field of scanned documents forgery detection. Eventually, various experiments were conducted and multiple metrics were used for the assessment of our contribution. We use the identical copy-move approach and error level analysis to localize duplicated blocks and inserted texts within documents respectively, while we exploit the image's metadata to track the manipulation traces. In this paper, we adapt the identical copy-move detection, error level analysis (ELA), and metadata analysis methods for the detection and localization of specific forgery cases in scanned documents images. the similar-looking characters and white identical background) impose new level of challenges for forgery detection. The distinct properties of documents (e.g.

Recently, an extensive research has been carried out in the field of digital images forgery detection, yet only a small portion specialized in the forgery detection in scanned documents images.

However, with the spread of forgery cases, their credibility started to be questionable. Our reliance on the scanned documents is no longer a deniable fact.

Jeffrey's image metadata viewer