  A scene text spotter is composed of text detection and recognition modules.
Many studies have been conducted to unify these modules into an end-to-end
trainable model to achieve better performance. A typical architecture places
detection and recognition modules into separate branches, and a RoI pooling is
commonly used to let the branches share a visual feature. However, there still
exists a chance of establishing a more complimentary connection between the
modules when adopting recognizer that uses attention-based decoder and detector
that represents spatial information of the character regions. This is possible
since the two modules share a common sub-task which is to find the location of
the character regions. Based on the insight, we construct a tightly coupled
single pipeline model. This architecture is formed by utilizing detection
outputs in the recognizer and propagating the recognition loss through the
detection stage. The use of character score map helps the recognizer attend
better to the character center points, and the recognition loss propagation to
the detector module enhances the localization of the character regions. Also, a
strengthened sharing stage allows feature rectification and boundary
localization of arbitrary-shaped text regions. Extensive experiments
demonstrate state-of-the-art performance in publicly available straight and
curved benchmark dataset.
