Word length-aware text spotting: Enhancing detection and recognition in
  dense text image
Hao Wang Huabing Zhou Yanduo Zhang Tao Lu Jiayi Ma
Scene text spotting is essential in various computer vision applications,
enabling extracting and interpreting textual information from images. However,
existing methods often neglect the spatial semantics of word images, leading to
suboptimal detection recall rates for long and short words within long-tailed
word length distributions that exist prominently in dense scenes. In this
paper, we present WordLenSpotter, a novel word length-aware spotter for scene
text image detection and recognition, improving the spotting capabilities for
long and short words, particularly in the tail data of dense text images. We
first design an image encoder equipped with a dilated convolutional fusion
module to integrate multiscale text image features effectively. Then,
leveraging the Transformer framework, we synergistically optimize text
detection and recognition accuracy after iteratively refining text region image
features using the word length prior. Specially, we design a Spatial Length
Predictor module (SLP) using character count prior tailored to different word
lengths to constrain the regions of interest effectively. Furthermore, we
introduce a specialized word Length-aware Segmentation (LenSeg) proposal head,
enhancing the network's capacity to capture the distinctive features of long
and short terms within categories characterized by long-tailed distributions.
Comprehensive experiments on public datasets and our dense text spotting
dataset DSTD1500 demonstrate the superiority of our proposed methods,
particularly in dense text image detection and recognition tasks involving
long-tailed word length distributions encompassing a range of long and short
words.