This paper proposes a new hierarchical MDL-based model for a joint detection and classi?cation of multilingual text lines in im- ages taken by hand-held cameras. The majority of related text detec- tion methods assume alphabet-based writing in a single language, e.g. in Latin. They use simple clustering heuristics speci?c to such texts: prox- imity between letters within one line, larger distance between separate lines, etc. We are interested in a significantly more ambiguous problem where images combine alphabet and logographic characters from multiple languages and typographic rules vary a lot (e.g. English, Korean, and Chinese). Complexity of detecting and classifying text lines in multiple languages calls for a more principled approach based on information- theoretic principles. Our new MDL model includes data costs combining geometric errors with classi?cation likelihoods and a hierarchical sparsity term based on label costs. This energy model can be e?ciently minimized by fusion moves. We demonstrate robustness of the proposed algorithm on a large new database of multilingual text images collected in the pub- lic transit system of Seoul.
Edge based methods retrieve an edge map (Sobel, Canny, Laplace) and then perform connected component (CC) analysis and outputs blobs. Moreover, Stroke width transform (SWT) @cite_18 @cite_23 also aims to find blobs that have consistent width of stroke. Color based methods, such as MSER and ER (inspired by MSER) assume that a text character's color is homogeneous. MSER was used by the ICDAR 2011 robust reading competition'' winner.
@cite_18 @cite_23
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The maximally stable extremal region (MSER) method has been widely used to extract character candidates, but because of its requirement for maximum stability, high text detection performance is difficult to obtain. To overcome this problem, we propose a robust character candidate extraction method that performs ER tree construction, sub-path partitioning, sub-path pruning, and character candidate selection sequentially. Then, we use the AdaBoost trained character classifier to verify the extracted character candidates. Then, we use heuristics to refine the classified character candidates and group the refined character candidates into text regions according to their geometric adjacency and color similarity. We also apply the proposed text detection method to two different color channels C r and C b and obtain the final detection result by combining the detection results on the three different channels. The proposed text detection method on ICDAR 2013 dataset achieved 8 , 1 , and 4 improvements in recall rate, precision rate and f-score, respectively, compared to the state-of-the-art 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. In this paper, we present a new approach for text localization in natural images, by discriminating text and non-text regions at three levels: pixel, component and text line levels. Firstly, a powerful low-level filter called the Stroke Feature Transform (SFT) is proposed, which extends the widely-used Stroke Width Transform (SWT) by incorporating color cues of text pixels, leading to significantly enhanced performance on inter-component separation and intra-component connection. Secondly, based on the output of SFT, we apply two classifiers, a text component classifier and a text-line classifier, sequentially to extract text regions, eliminating the heuristic procedures that are commonly used in previous approaches. The two classifiers are built upon two novel Text Covariance Descriptors (TCDs) that encode both the heuristic properties and the statistical characteristics of text stokes. Finally, text regions are located by simply thresholding the text-line confident map. Our method was evaluated on two benchmark datasets: ICDAR 2005 and ICDAR 2011, and the corresponding F-measure values are 0.72 and 0.73, respectively, surpassing previous methods in accuracy by a large margin. This paper presents a robust text detection approach based on color-enhanced contrasting extremal region (CER) and neural networks. Given a color natural scene image, six component-trees are built from its grayscale image, hue and saturation channel images in a perception-based illumination invariant color space, and their inverted images, respectively. From each component-tree, color-enhanced CERs are extracted as character candidates. By using a "divide-and-conquer" strategy, each candidate image patch is labeled reliably by rules as one of five types, namely, Long, Thin, Fill, Square-large and Square-small, and classified as text or non-text by a corresponding neural network, which is trained by an ambiguity-free learning strategy. After pruning unambiguous non-text components, repeating components in each component-tree are pruned further. Remaining components are then grouped into candidate text-lines and verified by another set of neural networks. Finally, results from six component-trees are combined, and a post-processing step is used to recover lost characters. Our proposed method achieves superior performance on both ICDAR-2011 and ICDAR-2013 "Reading Text in Scene Images" test sets. Several open problems in this topic are discussed and we present our solution.Color-enhanced CERs are effective to be candidate-text-connected-components.Neural networks work very well for the challenging text non-text classification.The "ambiguity-free learning" strategy addresses the ambiguity problem properly.The "divide-and-conquer" strategy solves the size normalization problem well.