Text detection in natural images is a challenging but necessary task for many applications. Existing approaches utilize large deep convolutional neural networks making it difficult to use them in real-world tasks. We propose a small yet relatively precise text extraction method. The basic component of it is a convolutional neural network which works in a fully-convolutional manner and produces results at multiple scales. Each scale output predicts whether a pixel is a part of some word, its geometry, and its relation to neighbors at the same scale and between scales. The key factor of reducing the complexity of the model was the utilization of depthwise separable convolution, linear bottlenecks, and inverted residuals. Experiments on public datasets show that the proposed network can effectively detect text while keeping the number of parameters in the range of 1.58 to 10.59 million in different configurations.
Zhou proposed a similar strategy in @cite_3 where a variety of postprocessing steps were eliminated by performing most of the calculations in a single U-Net-like @cite_12 FCN named EAST which outputs word box parameters by itself. Results of computations are filtered by non-maximum suppression (NMS) and thresholding. The length of the word to be detected is limited by a receptive field of output pixels.
@cite_12 @cite_3
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We present a novel single-shot text detector that directly outputs word-level bounding boxes in a natural image. We propose an attention mechanism which roughly identifies text regions via an automatically learned attentional map. This substantially suppresses background interference in the convolutional features, which is the key to producing accurate inference of words, particularly at extremely small sizes. This results in a single model that essentially works in a coarse-to-fine manner. It departs from recent FCN-based text detectors which cascade multiple FCN models to achieve an accurate prediction. Furthermore, we develop a hierarchical inception module which efficiently aggregates multi-scale inception features. This enhances local details, and also encodes strong context information, allowing the detector to work reliably on multi-scale and multi-orientation text with single-scale images. Our text detector achieves an F-measure of 77 on the ICDAR 2015 benchmark, advancing the state-of-the-art results in [18, 28]. Demo is available at: http: sstd.whuang.org . We propose a system that finds text in natural scenes using a variety of cues. Our novel data-driven method incorporates coarse-to-fine detection of character pixels using convolutional features (Text-Conv), followed by extracting connected components (CCs) from characters using edge and color features, and finally performing a graph-based segmentation of CCs into words (Word-Graph). For Text-Conv, the initial detection is based on convolutional feature maps similar to those used in Convolutional Neural Networks (CNNs), but learned using Convolutional k-means. Convolution masks defined by local and neighboring patch features are used to improve detection accuracy. The Word-Graph algorithm uses contextual information to both improve word segmentation and prune false character word detections. Different definitions for foreground (text) regions are used to train the detection stages, some based on bounding box intersection, and others on bounding box and pixel intersection. Our system obtains pixel, character, and word detection f-measures of 93.14 , 90.26 , and 86.77 respectively for the ICDAR 2015 Robust Reading Focused Scene Text dataset, out-performing state-of-the-art systems. This approach may work for other detection targets with homogenous color in natural scenes. In this paper, we develop a novel unified framework called DeepText for text region proposal generation and text detection in natural images via a fully convolutional neural network (CNN). First, we propose the inception region proposal network (Inception-RPN) and design a set of text characteristic prior bounding boxes to achieve high word recall with only hundred level candidate proposals. Next, we present a powerful textdetection network that embeds ambiguous text category (ATC) information and multilevel region-of-interest pooling (MLRP) for text and non-text classification and accurate localization. Finally, we apply an iterative bounding box voting scheme to pursue high recall in a complementary manner and introduce a filtering algorithm to retain the most suitable bounding box, while removing redundant inner and outer boxes for each text instance. Our approach achieves an F-measure of 0.83 and 0.85 on the ICDAR 2011 and 2013 robust text detection benchmarks, outperforming previous state-of-the-art results. The text of this paper has passed across many Internet routers on its way to the reader, but some routers will not pass it along unfettered because of censored words it contains. We present two sets of results: 1) Internet measurements of keyword filtering by the Great “Firewall” of China (GFC); and 2) initial results of using latent semantic analysis as an efficient way to reproduce a blacklist of censored words via probing. Our Internet measurements suggest that the GFC’s keyword filtering is more a panopticon than a firewall, i.e., it need not block every illicit word, but only enough to promote self-censorship. China’s largest ISP, ChinaNET, performed 83.3 of all filtering of our probes, and 99.1 of all filtering that occurred at the first hop past the Chinese border. Filtering occurred beyond the third hop for 11.8 of our probes, and there were sometimes as many as 13 hops past the border to a filtering router. Approximately 28.3 of the Chinese hosts we sent probes to were reachable along paths that were not filtered at all. While more tests are needed to provide a definitive picture of the GFC’s implementation, our results disprove the notion that GFC keyword filtering is a firewall strictly at the border of China’s Internet. While evading a firewall a single time defeats its purpose, it would be necessary to evade a panopticon almost every time. Thus, in lieu of evasion, we propose ConceptDoppler, an architecture for maintaining a censorship “weather report” about what keywords are filtered over time. Probing with potentially filtered keywords is arduous due to the GFC’s complexity and can be invasive if not done efficiently. Just as an understanding of the mixing of gases preceded effective weather reporting, understanding of the relationship between keywords and concepts is essential for tracking Internet censorship. We show that LSA can effectively pare down a corpus of text and cluster filtered keywords for efficient probing, present 122 keywords we discovered by probing, and underscore the need for tracking and studying censorship blacklists by discovering some surprising blacklisted keywords such as l�‡ (conversion rate), �„K— (Mein Kampf), and ýE0(NfT�� (International geological scientific federation (Beijing)).