| Popis: |
In a crime scene, document fragments with similar contents might lead to significant evidence. A criminalist when encounters such a scene with an enormous amount of torn document pieces, automated analysis becomes imperative in procuring potential evidence in a fast and reliable way. To analyze document fragments with similar contents, a processing module to segment the homogeneous zones based on the content type is a prerequisite. This paper proposes a deep learning-based module DAZeTD that can detect textual (printed/handwritten) and non-textual ragged zones. For classifying the content of the zones, we adopt the scheme of vision transformer; and to draw the zone boundaries, we employ outer isothetic cover. We created a dataset of 881 torn documents on which we performed rigorous experiments. We obtained an overall 87.71% mAP@0.5, which is quite promising. |
