The application of text recognition in the automatic analysis of invoices, contracts and other documents has significantly raised office efficiency, but the stamps overlapping with the texts in these documents may seriously degrade the recognition accuracy. To mitigate the negative effect, we propose a stamp eraser, which can simultaneously remove the stamps and recover the occluded texts. To better distinguish stamps from the complex background, we propose a stamp localization module to generate fine-grained binary masks for the eraser to focus more on stamps. This module can also provide the background textual information for recovering text with a skip connection. We also propose the dilated mask to make the generated image look more natural by filling the stamp area with pixels around the stamp. In addition, to evaluate the effectiveness of our method in boosting text recognition, we propose a synthetic data set for training and a real dataset with complex background for testing. Experiments have shown that our method can effectively improve the recognition accuracy of the text occluded with the stamps.

Recent scene text detection methods are almost based on deep learning and data-driven. Synthetic data is commonly adopted for pre-training due to expensive annotation cost. However, there are obvious domain discrepancies between synthetic data and real-world data. It may lead to suboptimal performance to directly adopt the model initialized by synthetic data in the fine-tuning stage. In this paper, we propose a new training paradigm for scene text detection, which introduces an UNsupervised Intermediate Training Stage (UNITS) that builds a buffer path to real-world data and can alleviate the gap between the pre-training stage and fine-tuning stage. Three training strategies are further explored to perceive information from real-world data in an unsupervised way. With UNITS, scene text detectors are improved without introducing any parameters and computations during inference. Extensive experimental results show consistent performance improvements on three public datasets.

Scene text spotting systems which integrate text detection and recognition modules have witnessed a lot of success in recent years. Existing works mostly follow the framework of word/character-level fine-grained detection and isolated-instance recognition, which overemphasize the role of detector and ignore the rich context information in recognition. After rethinking the conventional framework, and inspired by the glimpse-focus spotting pipeline of human beings, we ask:1) "can machine spot text without accurate detection just like human beings?", and if yes, 2) "is text block another alternative for scene text spotting other than word or character?". Based on these questions, we propose a new perspective of coarse-grained detection with multi-instance recognition for text spotting. Specifically, a pioneering network termed TextBlock is developed, and a heuristic text block generation method as well as a multi-instance block-level recognition module are proposed. In this way, the burden of detection is relieved, and the contextual semantic information is well explored for recognition. To train the block-level recognizer, a synthetic dataset including about 800K images is formed. As a by-product of attention, fine-grained detection can be recovered with the recognizer. Equipped with a detector without many bells and whistles (e.g., Faster R-CNN), TextBlock achieves competitive or even better performance compared with previous sophisticated text spotters on several public benchmarks. As a primary attempt, we expect this framework will have a potential impact on scene text spotting research in the future.

Accurate detection of multi-oriented text that accounts for a large proportion in real practice is of great significance. The performance has improved rapidly on common benchmarks in recent years. However, dense long text case and the quality of detection are easy to be overlooked. Direct regression may produce low-quality and incomplete detections due to the constrain of the receptive field; proposal-based methods could alleviate this but might introduce redundant context due to RoI operation, degrading the performance. To address the dilemma, a novel proposed corner-aware convolution in which the sampling positions tightly cover the text area is utilized to encode an initial corner prediction into the feature maps, which can be further used to produce a refined corner prediction. We embed the proposed module into an anchor-free baseline model, leading to a simple and effective fully convolutional corner refinement network (FC2RN). Experimental results on four public datasets including MSRATD500, ICDAR2015, RCTW-17, and COCO-Text demonstrate that FC2RN can outperform state-of-the-art methods.

To address the problem of density imbalance between nearby and faraway regions in point clouds, we propose a density-aware 3D single-stage object detector named Density-Net. First, a new 3D data augmentation method is designed to generate low-density areas by using the DBSCAN cluster method to acquire the cluster of point clouds, then by setting several different downsample ratios to stimulate sparse regions. By this way, there will be many sparse regions in point clouds. To restore the representative information, we propose Density-Set-Abstraction (Density-SA) to harmonize the high-level representative feature and low-level spatial feature, which boosts the final object detection performance. Moreover, we design a new Mask Sample branch to accomplish point sampling based on point-wise annotations, which increases the recall rate of meaningful points. We evaluate it on the widely used KITTI dataset. The experiments demonstrate that our method outperforms the well-established and highly-optimized 3DSSD (implemented by MMdetection3D) baseline 2.2% APs in moderate difficulty setting. At the same time, the runtime performance is also on par with 3DSSD. Code: https://github.com/Physu/density-net

Due to the large success in object detection and instance segmentation, Mask R-CNN attracts great attention and is widely adopted as a strong baseline for arbitrary-shaped scene text detection and spotting. However, two issues remain to be settled. The first is dense text case, which is easy to be neglected but quite practical. There may exist multiple instances in one proposal, which makes it difficult for the mask head to distinguish different instances and degrades the performance. In this work, we argue that the performance degradation results from the learning confusion issue in the mask head. We propose to use an MLP decoder instead of the "deconv-conv" decoder in the mask head, which alleviates the issue and promotes robustness significantly. And we propose instance-aware mask learning in which the mask head learns to predict the shape of the whole instance rather than classify each pixel to text or non-text. With instance-aware mask learning, the mask branch can learn separated and compact masks. The second is that due to large variations in scale and aspect ratio, RPN needs complicated anchor settings, making it hard to maintain and transfer across different datasets. To settle this issue, we propose an adaptive label assignment in which all instances especially those with extreme aspect ratios are guaranteed to be associated with enough anchors. Equipped with these components, the proposed method named MAYOR achieves state-of-the-art performance on five benchmarks including DAST1500, MSRA-TD500, ICDAR2015, CTW1500, and Total-Text.