In this paper, we introduce a novel end-end framework for multi-oriented scene text detection from an instanceaware semantic segmentation perspective. We present Fused Text Segmentation Networks, which combine multi-level features during the feature extracting as text instance may rely on finer feature expression compared to general objects. It detects and segments the text instance jointly and simultaneously, leveraging merits from both semantic segmentation task and region proposal based object detection task. Not involving any extra pipelines, our approach surpasses the current state of the art on multioriented scene text detection benchmarks: ICDAR2015 Incidental Scene Text and MSRA-TD500 reaching Hmean 84.1% and 82.0% respectively. Morever, we report a baseline on totaltext containing curved text which suggests effectiveness of the proposed approach.
The graphic processing unit (GPU) is becoming increasingly popular as a performance accelerator in various applications requiring high-performance parallel computing capability. In a central processing unit (CPU) or GPU hybrid system, software pipelining is a major task in order to deliver accelerated performance, where hiding CPU–GPU communication overheads by splitting a large task into small units is the key challenge. In this paper, we carry out a systematic investigation into task partitioning in order to achieve maximum performance gain. We first validate the advantage of even partition strategy, and then propose the optimal scheduling, with detailed study into how to achieve optimal unit size (data granularity) in an analytical framework. Experiments on AMD and NVIDIA GPU platforms demonstrate that our approaches achieve around 31 – 59% performance improvement using software pipelining.
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