Robust Scene Text Detection Research Articles

Robust Scene Text Detection for Partially Annotated Training Data

This article analyzed the impact of training data containing un-annotated text instances, i.e., partial annotation in scene text detection, and proposed a text region refinement approach to address it. Scene text detection is a problem that has attracted the attention of the research community for decades. Impressive results have been obtained for fully supervised scene text detection with recent deep learning approaches. These approaches, however, need a vast amount of completely labeled datasets, and the creation of such datasets is a challenging and time-consuming task. Research literature lacks the analysis of the partial annotation of training data for scene text detection. We have found that the performance of the generic scene text detection method drops significantly due to the partial annotation of training data. We have proposed a text region refinement method that provides robustness against the partially annotated training data in scene text detection. The proposed method works as a two-tier scheme. Text-probable regions are obtained in the first tier by applying hybrid loss that generates pseudo-labels to refine text regions in the second-tier during training. Extensive experiments have been conducted on a dataset generated from ICDAR 2015 by dropping the annotations with various drop rates and on a publicly available SVT dataset. The proposed method exhibits a significant improvement over the baseline and existing approaches for the partially annotated training data.

Fast Arbitrary Shaped Scene Text Detection via Text Discriminator

Robust scene text detection is one of the difficult and significant challenges in the computer vision community. Most previous methods detect arbitrary-shaped text using complicated post-processing steps. In this paper, we propose a trainable fast arbitrary-shaped text detection network by using the text discriminator, sharing visual information among the two complementary tasks. Specifically, we extend PSENet [1] by adding a text discriminator to fuse multiple predictions for each text instance, rather than using complicated post-processing steps which are time consuming. The text discriminator shares visual information with text detection network, and thus can achieve much faster detection speed compared with PSENet, while maintaining a similar accuracy reported in PSENet. Furthermore, our text discriminator can reduce the false alarms effectively. Experiments on ICDAR 2017 MLT, ICDAR 2015, and ICDAR 2019 ART datasets demonstrate that the proposed approach can achieve nearly realtime detection speed while keeping state-of-the-art detection accuracy.

Weather Prediction and Smart Notification System

In the recent period, many real-world applications and institutions generates a huge amount of data which is unstructured i.e., in the form of images containing data, receipts, invoices, forms, statements, contracts etc. This rich and detailed information presented in the text is of great significance in computer vision-based applications (driverless cars, assisting blind and visually impaired people, detecting labels and packages, automatic number plate recognition etc.). Recently, there has been a hike in the efforts, researches and progresses being done in this domain for its significance in data analysis and computer vision. Here has been a diversity of challenges in unstructured data like image sensor noise, different viewing angles, blur, lighting conditions, resolution, and non-planar object. Our objective for taking up this topic for research are i) to detect and recognize the text from the data ii) to handle diversity and variability of text in natural scene iii) to explore various datasets iv) to deal with various issues occurring in scene text detection. To tackle this problem, we propose a robust scene text detection and recognition method with adaptive text region representation using deep learning model open CV with EAST algorithm as detection pipeline and tesseract. The recurrent neural network-based adaptive text region representation is proposed for text region refinement, where a pair of boundary points are predicted each time step until no new points are found. In this way, text regions in an image are detected and represented with the adaptive number of boundary points.

Bottom-Up Scene Text Detection with Markov Clustering Networks

A novel detection framework named Markov Clustering Network (MCN) is proposed for fast and robust scene text detection. Different from the traditional top-down scene text detection approaches that inherit from the classic object detection, MCN detects scene text objects in a bottom-up manner. MCN predicts instance-level bounding boxes by firstly converting an image into a stochastic flow graph where Markov Clustering is performed based on the predicted stochastic flows. The stochastic flows encode the local correlation and semantic information of scene text objects. An object is modeled as strongly connected nodes by flows, which allows flexible and bottom-up detection for scale-varying and rotated text objects without prior knowledge of object size. The flow prediction is supported by the advanced Convolutional Neural Networks architectures and Position-aware spatial attention mechanism, which provides enhanced flow prediction by adaptively fusing spatial representations. The experimental evaluation on public benchmarks shows that our MCN method achieves the state-of-art performance on public benchmarks, especially in retrieving long and oriented texts.