BigyaPAn: Deep Analysis of Old Paper Advertisement

Publisher:
IEEE
Publication Type:
Conference Proceeding
Citation:
2021 International Joint Conference on Neural Networks (IJCNN), 2021, 2021-July
Issue Date:
2021-09-20
Filename Description Size
BigyaPAn_Deep_Analysis_of_Old_Paper_Advertisement.pdfPublished version15.3 MB
Adobe PDF
Full metadata record
In this paper, we work on analyzing old paper advertisement (Ad). An Ad usually contains various types of textual and non-textual objects, which may also be in different orientations. We attempt to detect such objects from an early Indian-print paper Ad database comprising 1500 Ad images. The past major object detectors did not perform well on this database. We propose a deep reinforcement learning-based orientation-aware object detector. Our system learns by itself where to look and what to look of an Ad image. Therefore, it can bypass the impeding zone due to degraded image quality. To find the looking spot, we come up with a foveal transformation. In reinforcement learning, we present a scheme for shaping an internal reward with a top-up. For oriented object detection, we also propose a generic loss function. Our system obtained encouraging results from the experiments performed on the Ad database.
Please use this identifier to cite or link to this item: