  State-of-the-art text spotting systems typically aim to detect isolated words
or word-by-word text in images of natural scenes and ignore the semantic
coherence within a region of text. However, when interpreted together,
seemingly isolated words may be easier to recognize. On this basis, we propose
a novel "semantic-based text recognition" (STR) deep learning model that reads
text in images with the help of understanding context. STR consists of several
modules. We introduce the Text Grouping and Arranging (TGA) algorithm to
connect and order isolated text regions. A text-recognition network interprets
isolated words. Benefiting from semantic information, a sequenceto-sequence
network model efficiently corrects inaccurate and uncertain phrases produced
earlier in the STR pipeline. We present experiments on two new distinct
datasets that contain scanned catalog images of interior designs and
photographs of protesters with hand-written signs, respectively. Our results
show that our STR model outperforms a baseline method that uses
state-of-the-art single-wordrecognition techniques on both datasets. STR yields
a high accuracy rate of 90% on the catalog images and 71% on the more difficult
protest images, suggesting its generality in recognizing text.
