Christina M Swords, Jerlym S Porter, Amy J Hawkins, Edwin Li, Melissa Rowland-Goldsmith, Matthew D Koci, John T Tansey, Nicole C Woitowich
Science Communication Training Imparts Confidence and Influences Public Engagement Activity
Journal of microbiology & biology education
text instance one category selfdescribed objects provides valuable information understanding describing cluttered scenes rich precise highlevel semantics embodied text could drastically benefit understanding world around us recent visual phrase grounding approaches focus general objects paper explores extracting designated texts predicting unambiguous scene text information ie accurately localize recognize specific targeted text instance cluttered image natural language descriptions referring expressions address issue first novel recurrent dense text localization network dtln proposed sequentially decode intermediate convolutional representations cluttered scene image set distinct text instance detections approach avoids repeated text detections multiple scales recurrently memorizing previous detections effectively tackles crowded text instances close proximity second propose context reasoning text retrieval crtr model jointly encodes text instances context information recurrent network ranks localized text bounding boxes scoring function context compatibility third recurrent text recognition module introduced extend applicability aforementioned dtln crtr models via text verification transcription quantitative evaluations standard scene text extraction benchmarks newly collected scene text retrieval dataset demonstrate effectiveness advantages models joint scene text localization retrieval recognition task