  We present a novel deep neural model for text detection in document images.
For robust text detection in noisy scanned documents, the advantages of
multi-task learning are adopted by adding an auxiliary task of text
enhancement. Namely, our proposed model is designed to perform noise reduction
and text region enhancement as well as text detection. Moreover, we enrich the
training data for the model with synthesized document images that are fully
labeled for text detection and enhancement, thus overcome the insufficiency of
labeled document image data. For the effective exploitation of the synthetic
and real data, the training process is separated in two phases. The first phase
is training only synthetic data in a fully-supervised manner. Then real data
with only detection labels are added in the second phase. The enhancement task
for the real data is weakly-supervised with information from their detection
labels. Our methods are demonstrated in a real document dataset with
performances exceeding those of other text detection methods. Moreover,
ablations are conducted and the results confirm the effectiveness of the
synthetic data, auxiliary task, and weak-supervision. Whereas the existing text
detection studies mostly focus on the text in scenes, our proposed method is
optimized to the applications for the text in scanned documents.
