Linear Object Detection in Document Images using Multiple Object
  Tracking
Philippe Bernet Joseph Chazalon Edwin Carlinet Alexandre Bourquelot Elodie Puybareau
Linear objects convey substantial information about document structure, but
are challenging to detect accurately because of degradation (curved, erased) or
decoration (doubled, dashed). Many approaches can recover some vector
representation, but only one closed-source technique introduced in 1994, based
on Kalman filters (a particular case of Multiple Object Tracking algorithm),
can perform a pixel-accurate instance segmentation of linear objects and enable
to selectively remove them from the original image. We aim at re-popularizing
this approach and propose: 1. a framework for accurate instance segmentation of
linear objects in document images using Multiple Object Tracking (MOT); 2.
document image datasets and metrics which enable both vector- and pixel-based
evaluation of linear object detection; 3. performance measures of MOT
approaches against modern segment detectors; 4. performance measures of various
tracking strategies, exhibiting alternatives to the original Kalman filters
approach; and 5. an open-source implementation of a detector which can
discriminate instances of curved, erased, dashed, intersecting and/or
overlapping linear objects.