  The prevalent scene text detection approach follows four sequential steps
comprising character candidate detection, false character candidate removal,
text line extraction, and text line verification. However, errors occur and
accumulate throughout each of these sequential steps which often lead to low
detection performance. To address these issues, we propose a unified scene text
detection system, namely Text Flow, by utilizing the minimum cost (min-cost)
flow network model. With character candidates detected by cascade boosting, the
min-cost flow network model integrates the last three sequential steps into a
single process which solves the error accumulation problem at both character
level and text line level effectively. The proposed technique has been tested
on three public datasets, i.e, ICDAR2011 dataset, ICDAR2013 dataset and a
multilingual dataset and it outperforms the state-of-the-art methods on all
three datasets with much higher recall and F-score. The good performance on the
multilingual dataset shows that the proposed technique can be used for the
detection of texts in different languages.
