Abstract:

Text detection plays an important role in image understanding,and deep-learning-based algorithms are popular methods including single-stage and two-stage methods.Usually,two-stage based text detection methods have a higher accuracy than the single stage based methods.The two-stage text detection method usually contains the feature pooling operation in the region of interests(RoI),which provides the local region features with fixed dimensions for further detection and recognition tasks.However,for complex text areas such as a curved text,the existing pooling methods based on the rectangular RoI are no longer applicable.Using point features instead of area features to solve the problem loses spatial information compared with area features.To address this issue,we propose a complex text region detection method based on polygon feature pooling and Transformer.First,we extend the feature pooling shape of RoI from the rectangle to the polygon,which does not need any shape fitting.and the features of polygon RoI with fixed dimensions are pooled,which avoids the error in the fitting process.Furthermore,the pooled polygon region features are regarded as context-sensitive sequences,which are input to the Transformer to fuse the context of the visual feature to reduce the training difficulties and improves the detection accuracy.Our experiments on the complex text region datasets,such as ICDAR2015,MLT,Total Text and CTW1500,show that the proposed two-stage detection algorithm can extract the features of RoI very well and achieves better detection results than the state-of-the-art methods.

Key words: text region detection, two-stage methods, polygon, feature pooling, Transformer