Enhancing Energy Minimization Framework for Scene Text Recognition with Top-Down Cues

Recognizing scene text is a challenging problem, even more so than the recognition of scanned documents. This problem has gained significant attention from the computer vision community in recent years, and several methods based on energy minimization frameworks and deep learning approaches have been proposed. In this work, we focus on the energy minimization framework and propose a model that exploits both bottom-up and top-down cues for recognizing cropped words extracted from street images. The bottom-up cues are derived from individual character detections from an image. We build a conditional random field model on these detections to jointly model the strength of the detections and the interactions between them. These interactions are top-down cues obtained from a lexicon-based prior, i.e., language statistics. The optimal word represented by the text image is obtained by minimizing the energy function corresponding to the random field model. We evaluate our proposed algorithm extensively on a number of cropped scene text benchmark datasets, namely Street View Text, ICDAR 2003, 2011 and 2013 datasets, and IIIT 5K-word, and show better performance than comparable methods. We perform a rigorous analysis of all the steps in our approach and analyze the results. We also show that state-of-the-art convolutional neural network features can be integrated in our framework to further improve the recognition performance

Automated Top View Registration of Broadcast Football Videos

In this paper, we propose a novel method to register football broadcast video frames on the static top view model of the playing surface. The proposed method is fully automatic in contrast to the current state of the art which requires manual initialization of point correspondences between the image and the static model. Automatic registration using existing approaches has been difficult due to the lack of sufficient point correspondences. We investigate an alternate approach exploiting the edge information from the line markings on the field. We formulate the registration problem as a nearest neighbour search over a synthetically generated dictionary of edge map and homography pairs. The synthetic dictionary generation allows us to exhaustively cover a wide variety of camera angles and positions and reduce this problem to a minimal per-frame edge map matching procedure. We show that the per-frame results can be improved in videos using an optimization framework for temporal camera stabilization. We demonstrate the efficacy of our approach by presenting extensive results on a dataset collected from matches of football World Cup 2014

Image Retrieval using Textual Cues

International audienceWe present an approach for the text-to-image retrieval problem based on textual content present in images. Given the recent developments in understanding text in images, an appealing approach to address this problem is to localize and recognize the text, and then query the database, as in a text retrieval problem. We show that such an approach, despite being based on state-of-the-art methods, is insufficient, and propose a method, where we do not rely on an exact localization and recognition pipeline. We take a query-driven search approach, where we find approximate locations of characters in the text query, and then impose spatial constraints to generate a ranked list of images in the database. The retrieval performance is evaluated on public scene text datasets as well as three large datasets, namely IIIT scene text retrieval, Sports-10K and TV series-1M, we introduce

A giant submandibular sialolith - How to manage?

The presence of a sialolith is one of the most common diseases of salivary gland. It is relatively common in submandibular salivary glands and its duct. This case report is of a patient who presented at our unit with a history of severe pain and swelling on floor of the mouth, which was clinically and radiographically diagnosed as a sialolith. The diagnostic and treatment protocol in managing a patient with a giant sialolith is enumerated in this manuscript

Scene Text Recognition using Higher Order Language Priors

International audienceThe problem of recognizing text in images taken in the wild has gained significant attention from the computer vision community in recent years. Contrary to recognition of printed documents, recognizing scene text is a challenging problem. We focus on the problem of recognizing text extracted from natural scene images and the web. Significant attempts have been made to address this problem in the recent past. However, many of these works benefit from the availability of strong context, which naturally limits their applicability. In this work we present a framework that uses a higher order prior computed from an English dictionary to recognize a word, which may or may not be a part of the dictionary. We show experimental results on publicly available datasets. Furthermore, we introduce a large challenging word dataset with five thousand words to evaluate various steps of our method exhaustively. The main contributions of this work are: (1) We present a framework, which incorporates higher order statistical language models to recognize words in an unconstrained manner (i.e. we overcome the need for restricted word lists, and instead use an English dictionary to compute the priors). (2) We achieve significant improvement (more than 20%) in word recognition accuracies without using a restricted word list. (3) We introduce a large word recognition dataset (atleast 5 times larger than other public datasets) with character level annotation and benchmark it

An MRF Model for Binarization of Natural Scene Text

International audienceInspired by the success of MRF models for solving object segmentation problems, we formulate the binarization problem in this framework. We represent the pixels in a document image as random variables in an MRF, and introduce a new energy (or cost) function on these variables. Each variable takes a foreground or background label, and the quality of the binarization (or labelling) is determined by the value of the energy function. We minimize the energy function, i.e. find the optimal binarization, using an iterative graph cut scheme. Our model is robust to variations in foreground and background colours as we use a Gaussian Mixture Model in the energy function. In addition, our algorithm is efficient to compute, and adapts to a variety of document images. We show results on word images from the challenging ICDAR 2003 dataset, and compare our performance with previously reported methods. Our approach shows significant improvement in pixel level accuracy as well as OCR accuracy

Interleukin-12B & interleukin-10 gene polymorphisms in pulmonary tuberculosis

Background & objectives: Cytokines play an important role in anti-tuberculosis immune response. Skewing of immunity from protective to pathogenic may involve a shift in Th1-Th2 paradigm. Cytokine gene polymorphism is known to be associated with functional differences in cytokine regulation and altered clinical performance in a variety of diseases. The aim of this study was to know whether Interleukin-12B 3’ UTR (Taq1) (A/C) and Interleukin-10 (-1082 G/A) gene polymorphisms were associated with susceptibility to pulmonary tuberculosis. Methods: IL -10 (-1,082 G/A) and IL-12B gene polymorphisms were studied in132 pulmonary TB (PTB) patients and 143 normal healthy subjects (NHS), using DNA based polymerase chain reaction (PCR) with sequence specific primers and restriction digestion. Results: The allelic as well as genotypic frequencies of Interleukin -10 (-1082) and Interleukin -12B (3’UTR Taq 1) did not differ significantly between the patients and controls. Interpretation & conclusion: Our findings suggested that IL -10 (-1082 G/A) and IL -12B 3’UTR (Taq I) (A/C) gene polymorphisms were not associated either with susceptibility or resistance to pulmonary tuberculosis in the south Indian population

Scene Text Recognition and Retrieval for Large Lexicons

International audienceIn this paper we propose a framework for recognition and retrieval tasks in the context of scene text images. In contrast to many of the recent works, we focus on the case where an image-specific list of words, known as the small lexicon setting, is unavailable. We present a conditional random field model defined on potential character locations and the interactions between them. Observing that the interaction potentials computed in the large lexicon setting are less effective than in the case of a small lexicon, we propose an iterative method, which alternates between finding the most likely solution and refining the interaction po-tentials. We evaluate our method on public datasets and show that it improves over baseline and state-of-the-art approaches. For example, we obtain nearly 15% improvement in recognition accuracy and precision for our retrieval task over baseline methods on the IIIT-5K word dataset, with a large lexicon containing 0.5 million words

Interferon gamma (IFN -gamma) and interleukin -4 (IL-4) gene variants and cytokine levels in pulmonary tuberculosis. Indian Journal of Medical Research

Background & objectives: Cytokine gene polymorphisms may alter Th1/Th2 balance with major implications in tuberculosis. The aim of our study was to find out whether Interferon � +874A and IL-4 -590T polymorphisms were associated with susceptibility to pulmonary tuberculosis as well as the level of IFN� and IL-4 in south Indian population. Methods: Interferon � +874A and IL-4 -590T promoter polymorphisms were studied in 129 pulmonary tuberculosis (PTB) patients and 127 normal healthy subjects (NHS) and were associated with culture filtrate and live Mycobacterium tuberculosis induced IFN� and IL-4 production in peripheral blood mononuclear cells (PBMCs). IL-4 gene variants were also associated with IgG antibody levels against M. tuberculosis culture filtrate antigen. Results: The variant IFN� genotypes and IFN� levels between genotypes did not differ significantly in patients and controls. Significantly increased frequency of variant IL-4 ‘CT’ genotype in PTB patients (P<0.05) and ‘CC’ genotype in control group (P<0.01) was observed. IL-4 levels were detectable in very few subjects and the IgG levels did not differ between the three IL-4 genotypes. Interpretation & conclusion: The study suggests a lack of functional association of Interferon � +874A polymorphism in tuberculosis in south Indian population. The higher frequency of IL-4 ‘CT’ genotype in PTB suggests a possible association of IL-4 -590T promoter polymorphism with susceptibility to tuberculosis, and the ‘CC’ genotype may be associated with protection

Whole is Greater than Sum of Parts: Recognizing Scene Text Words

International audienceRecognizing text in images taken in the wild is a challenging problem that has received great attention in recent years. Previous methods addressed this problem by first detecting individual characters, and then forming them into words. Such approaches often suffer from weak character detections, due to large intra-class variations, even more so than characters from scanned documents. We take a different view of the problem and present a holistic word recognition framework. In this, we first represent the scene text image and synthetic images generated from lexicon words using gradient-based features. We then recognize the text in the image by matching the scene and synthetic image features with our novel weighted Dynamic Time Warping (wDTW) approach. We perform experimental analysis on challenging public datasets, such as Street View Text and ICDAR 2003. Our proposed method significantly outperforms our earlier work in Mishra et al. (CVPR 2012), as well as many other recent works, such as Novikova et al. (ECCV 2012), Wang et al. (ICPR 2012), Wang et al. (ICCV 2011)