Text Localization and Extraction in Natural Scene Images

Content based image analysis methods are receiving more attention in recent time due to increase in use of image capturing devices. Among all contents in images, text data finds wide applications such as license plate reading, mobile text recognition and so on. The Text Information Extraction (TIE) is a process of extraction of text from images which consists of four stages: detection, localization, extraction and recognition. Text detection and localization plays important role in system’s performance. The existing methods for detection and localization, region based and connected component based, have some limitations due difference in size, style, orientation etc. To overcome the limitations, a hybrid approach is proposed to detect and localize text in natural scene images. This approach includes steps: pre-processing connected component analysis, text extraction. DOI: 10.17762/ijritcc2321-8169.15021

Directional correlation analysis of local Haar binary pattern for text detection

Two main restrictions exist in state-of-the-art text detection algorithms: 1. Illumination variance; 2. Text-background contrast variance. This paper presents a robust text characterization approach based on local Haar binary pattern (LHBP) to address these problems. Based on LHBP, a coarse-to-fine detection framework is presented to precisely locate text lines in scene images. Firstly, threshold-restricted local binary pattern is extracted from high-frequency coefficients of pyramid Haar wavelet. It preserves and uniforms inconsistent text-background contrasts while filtering gradual illumination variations. Subsequently, we propose a directional correlation analysis (DCA) approach to filter non-directional LHBP regions for locating candidate text regions. Finally, using LHBP histogram, an SVM-based post-classification is presented to refine detection results. Experimental results on ICDAR 03 demonstrate the effectiveness and robustness of our proposed method. Index Terms—Text detection, pyramid wavelet, local binary pattern, directional correlation analysis, SV

Using context to resolve ambiguity in sketch understanding

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (p. 48-49).This thesis presents methods for improving sketch understanding, without knowledge of a domain or the particular symbols being used in the sketch, by recognizing common sketch primitives. We address two issues that complicate recognition in its early stages. The first is imprecision and inconsistencies within a single sketch or between sketches by the same person. This problem is addressed with a graphical model approach that incorporates limited knowledge of the surrounding area in the sketch to better decide the intended meaning of a small piece of the sketch. The second problem, that of variation among sketches with different authors, is addressed by forming groups from the authors in training set. We apply these methods to the problem of finding corners, a common sketch primitive, and describe how this can translate into better recognition of entire sketches. We also describe the collection of a data set of sketches.by Sonya J. Cates.S.M