Neighborhood Structure-Based Model for Multilingual Arbitrarily-Oriented Text Localization in Images/Videos

The text matter in an image or a video provides more important clue and semantic information of the particular event in the actual situation. Text localization task stands an interesting and challenging research-oriented process in the zone of image processing due to irregular alignments, brightness, degradation, and complexbackground. The multilingual textual information has different types of geometrical shapes and it makes further complex to locate the text information. In this work, an effective model is presented to locate the multilingual arbitrary oriented text. The proposed method developed a neighborhood structure model to locate the text region. Initially, the maxmin cluster is applied along with 3X3 sliding window to sharpen the text region. The neighborhood structure creates the boundary for every component using normal deviation calculated from the sharpened image. Finally, the double stroke structure model is employed to locate the accurate text region. The presented model is analyzed on five standard datasets such as NUS, arbitrarily oriented text, Hua's, MRRC and real-time video dataset with performance metrics such as recall, precision, and f-measure

Text Localization in Video Using Multiscale Weber's Local Descriptor

In this paper, we propose a novel approach for detecting the text present in videos and scene images based on the Multiscale Weber's Local Descriptor (MWLD). Given an input video, the shots are identified and the key frames are extracted based on their spatio-temporal relationship. From each key frame, we detect the local region information using WLD with different radius and neighborhood relationship of pixel values and hence obtained intensity enhanced key frames at multiple scales. These multiscale WLD key frames are merged together and then the horizontal gradients are computed using morphological operations. The obtained results are then binarized and the false positives are eliminated based on geometrical properties. Finally, we employ connected component analysis and morphological dilation operation to determine the text regions that aids in text localization. The experimental results obtained on publicly available standard Hua, Horizontal-1 and Horizontal-2 video dataset illustrate that the proposed method can accurately detect and localize texts of various sizes, fonts and colors in videos.Comment: IEEE SPICES, 201

Combined Contrast Enhanced and Wide-Baseline Technique for Kannada Text Detection in Images

  Text characters contained in images are a valuable source of information for content-based indexing and retrieval applications. These text characters are difficult to identify and distinguish due to their various sizes, grayscale values, and intricate backgrounds. The paper presents a new method for identifying text contained in images of any grayscale value. The proposed scheme uses a combination of contrast-limited adaptive histogram equalization (CLAHE) algorithm, which enhances the local contrast and limits any noise in the image, and the wide baseline image matching technique which helps locate an object in the image. Applying a series of morphological operations and filtering at each stage, the resultant component is the detected text which is either a character, word or a line segment. MATLAB based simulation and evaluation on a self-curated Kannada, a popular south Indian language and other standard datasets proves that the proposed technique outperforms other methods consistently on precision, recall and F1-score. Importantly, on the Kannada dataset, it returns the highest recall of 98% since the system is specifically tuned for its linguistic features proving its robustness. Further, the proposed technique can be extended to image pre-processing tasks for deep learning models to improve their accuracy and for text recognition tasks

Spatio-Temporal Land-Cover Change Analysis of the Imo River Estuarine Wetlands and its Implication on the Mangrove Ecosystem

Land-cover in coastal areas determines to an extent the degree its surrounding water bodies differ from their pristine nature. Change mapping within coastal wetlands especially in areas prone to anthropogenic activities will help protect these fragile habitats. A land-cover change analysis was carried out on the Imo River estuary covering about 724 hectares of the estuarine wetlands, using 3 satellite images of the area acquired from Landsat 5TM (1986), Landsat 7 ETM (2000) and Landsat 8 OLI (2016) for a period of 30 years. Results showed that mangrove vegetation decreased by 17.5%, crop and grassland increased by 17% while total coverage of settlements increased by 3.4%. This implies that mangrove vegetation has significantly depleted over time and if this trend is not checked could lead to increase siltation of the river through runoff as a result of deforestation. There is also a likelihood of increase waste discharge directly or indirectly into the river by neighboring communities. It is recommended that proper waste management practices be adopted to suit urban growth within these communities and constant monitoring should be carried out using high resolution images to monitor coastal wetlands to protect fishes and other endangered species

Text localization in web images using probabilistic candidate selection model

Master'sMASTER OF SCIENC