Seeing Text in the Dark: Algorithm and Benchmark

arXiv:2404.08965v1 Announce Type: new
Abstract: Localizing text in low-light environments is challenging due to visual degradations. Although a straightforward solution involves a two-stage pipeline with low-light image enhancement (LLE) as the initial step followed by detector, LLE is primarily designed for human vision instead of machine and can accumulate errors. In this work, we propose an efficient and effective single-stage approach for localizing text in dark that circumvents the need for LLE. We introduce a constrained learning module as an …

abstract algorithm arxiv benchmark cs.cv cs.mm environments errors human image light low machine pipeline solution stage text type vision visual