The absence of large scale datasets with pixel-level supervisions is a significant obstacle for the training of deep convolutional networks for scene text segmentation. For this reason, synthetic data generation is normally employed to enlarge the training dataset. Nonetheless, synthetic data cannot reproduce the complexity and variability of natural images. In this paper, a weakly supervised learning approach is used to reduce the shift between training on real and synthetic data. Pixel-level supervisions for a text detection dataset (i.e. where only bounding-box annotations are available) are generated. In particular, the COCO-Text-Segmentation (COCO_TS) dataset, which provides pixel-level supervisions for the COCO-Text dataset, is created and released. The generated annotations are used to train a deep convolutional neural network for semantic segmentation. Experiments show that the proposed dataset can be used instead of synthetic data, allowing us to use only a fraction of the training samples and significantly improving the performances.
Document image segmentation has a long history and was originally based on thresholding approaches (local, global or adaptive) @cite_1 @cite_21 @cite_17 . The application of these methods to scene text segmentation is quite challenging, due to the high variability of conditions that can be found in natural images. To face this variability, in @cite_22 , low level features are used to identify the seed points of texts and backgrounds and then to segment the text using semi--supervised learning. In @cite_14 , the binarization of scene text has been formulated as a Markov Random Field model optimization problem, where the optimal binarization is obtained iteratively with Graph Cuts. To improve the segmentation performance, a multilevel maximally stable extremal region approach, applied together with a text candidate selection algorithm based on hand--extracted text--specific features, has been presented in @cite_18 . Finally, in @cite_20 , a CNN approach to scene text segmentation is described, which employs three stages for extraction, refinement and classification.
@cite_18 @cite_14 @cite_22 @cite_21 @cite_1 @cite_20 @cite_17
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The segmentation of scene text from the image background has shown great importance in scene text recognition. In this paper, we propose a multi-level MSER technology that identifies the best-quality text candidates from a set of stable regions that are extracted from different color channel images. In order to identify the best-quality text candidates, a segmentation score is defined which exploits four measures to evaluate the text probability of each stable region including: 1) Stroke width that measures the small stroke width variation of the text, 2) Boundary curvature that measures the smoothness of the stable region boundary, 3) Character confidence that measures the likelihood of a stable region being text based on a pre-trained support vector classifier, 4) Color constancy that measures the global color consistency of each selected text candidate. Finally, the MSERs with the best segmentation score from each channel are combined to form the final segmentation. The proposed method is evaluated on the ICDAR2003 and SVT datasets and experiments show that it outperforms both popular document image binarization methods and state of the art scene text segmentation methods. 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. This paper presents a novel preprocessing method based on mathematical morphology techniques to improve the subsequent thresholding quality of raw degraded word images. The raw degraded word images contain undesirable shapes called critical shadows on the background that cause noise in binary images. This noise constitutes obstacles to posterior segmentation of characters. Direct application of a thresholding method produces inadequate binary versions of these degraded word images. Our preprocessing method called Shadow Location and Lightening (SL*L) adaptively, accurately and without manual fine-tuning of parameters locates these critical shadows on grayscale degraded images using morphological operations, and lightens them before applying eventual thresholding process. In this way, enhanced binary images without unpredictable and inappropriate noise can be provided to subsequent segmentation of characters. Then, adequate binary characters can be segmented and extracted as input data to optical character recognition (OCR) applications saving computational effort and increasing recognition rate. The proposed method is experimentally tested with a set of several raw degraded images extracted from real photos acquired by unsophisticated imaging systems. A qualitative analysis of experimental results led to conclusions that the thresholding result quality was significantly improved with the proposed preprocessing method. Also, a quantitative evaluation using a testing data of 1194 degraded word images showed the essentiality and effectiveness of the proposed preprocessing method to increase segmentation and recognition rates of their characters. Furthermore, an advantage of the proposed method is that Otsu's method as a simple and easily implementable global thresholding technique can be sufficient to reducing computational load. Scene text extraction, i.e., segmenting text pixels from background, is an important step before the text can be recognized. It is a challenging problem due to the cluttered background and the variation of lighting. In this paper, we propose a seed-based segmentation method that can automatically judge the text polarity, extract seed points of text and background, and segment texts by semi-supervised learning (SSL). First, we estimate the text polarity and the stroke width using gradient local correlation. Then, all the points in the middle of stroke edge pairs satisfying the width and polarity are taken as foreground seeds, and the points in the middle of the edge pairs with opposite polarity are taken as background seeds. The whole image is then segmented into text and background using an SSL algorithm. Owing to the accurate estimate of text polarity and extraction of seed points, the proposed method yields good segmentation performance. Experimental results on the KAIST dataset demonstrate the superiority of the method.