An energy minimization based approach for scene text recognition with seamless integration of multiple cues.Applied also to the challenging open vocabulary setting, where a word-specific lexicon is unavailable.Comprehensive experimental evaluation on several state-of-the-art benchmarks. 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.
A study on human reading psychology shows that our reading improves significantly with prior knowledge of the language @cite_30 . Motivated by such studies, OCR systems have used, often in post-processing steps @cite_40 @cite_16 , statistical language models like @math -grams to improve their performance. Bigrams or trigrams have also been used in the context of scene text recognition as a post-processing step, e.g., @cite_8 . A few other works @cite_80 @cite_17 @cite_43 integrate character recognition and linguistic knowledge to deal with recognition errors. For example, @cite_80 computes @math -gram probabilities from more than 100 million characters and uses a Viterbi algorithm to find the correct word. The method in @cite_43 , developed in the same year as our CVPR 2012 work @cite_50 , builds a graph on potential character locations and uses @math -gram scores to constrain the inference algorithm to predict the word. In contrast, our approach uses a novel location-specific prior (cf. )).
@cite_30 @cite_8 @cite_43 @cite_40 @cite_50 @cite_80 @cite_16 @cite_17
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Contents: Preface. Part I: Background Information. Introduction and Preliminary Information. Writing Systems. Word Perception. Part II: Skilled Reading of Text. The Work of the Eyes. Eye-Movement Control During Reading. Inner Speech. Part III: Understanding Text. Words and Sentences. Representation of Discourse. Part IV: Beginning Reading and Reading Disability. Learning to Read. Stages of Reading Development. Dyslexia. Part V: Toward a Model of Reading. Speedreading, Proofreading, and Individual Differences. Models of Reading. With the increasing market of cheap cameras, natural scene text has to be handled in an efficient way. Some works deal with text detection in the image while more recent ones point out the challenge of text extraction and recognition. We propose here an OCR correction system to handle traditional issues of recognizer errors but also the ones due to natural scene images, i.e. cut characters, artistic display, incomplete sentences (present in advertisements) and out- of-vocabulary (OOV) words such as acronyms and so on. The main algorithm bases on finite-state machines (FSMs) to deal with learned OCR confusions, capital accented letters and lexicon look-up. Moreover, as OCR is not considered as a black box, several outputs are taken into account to intermingle recognition and correction steps. Based on a public database of natural scene words, detailed results are also presented along with future works. Understanding text captured in real-world scenes is a challenging problem in the field of visual pattern recognition and continues to generate a significant interest in the OCR (Optical Character Recognition) community. This paper proposes a novel method to recognize scene texts avoiding the conventional character segmentation step. The idea is to scan the text image with multi-scale windows and apply a robust recognition model, relying on a neural classification approach, to every window in order to recognize valid characters and identify non valid ones. Recognition results are represented as a graph model in order to determine the best sequence of characters. Some linguistic knowledge is also incorporated to remove errors due to recognition confusions. The designed method is evaluated on the ICDAR 2003 database of scene text images and outperforms state-of-the-art approaches. A technique is presented that uses visual relationships between word images in a document to improve the recognition of the text it contains. This technique takes advantage of the visual relationships between word images that are usually lost in most conventional optical character recognition (OCR) techniques. The visual relations are defined to be the equivalence that exists between images of the same word or portions of word images. An algorithm is presented that calculates these relationships in a document. The resulting clusters are integrated with the recognition results provided by an OCR system. Inconsistencies in OCR results between equivalent images are identified and used to improve recognition performance. Experimental results are presented in which the input is provided directly from a commercial OCR system. Scene text recognition has gained significant attention from the computer vision community in recent years. Recognizing such text is a challenging problem, even more so than the recognition of scanned documents. In this work, we focus on the problem of recognizing text extracted from street images. We present a framework that exploits both bottom-up and top-down cues. The bottom-up cues are derived from individual character detections from the image. We build a Conditional Random Field model on these detections to jointly model the strength of the detections and the interactions between them. We impose top-down cues obtained from a lexicon-based prior, i.e. language statistics, on the model. The optimal word represented by the text image is obtained by minimizing the energy function corresponding to the random field model. We show significant improvements in accuracies on two challenging public datasets, namely Street View Text (over 15 ) and ICDAR 2003 (nearly 10 ). This paper describes a mobile device which tries to give the blind or visually impaired access to text information. Three key technologies are required for this system: text detection, optical character recognition, and speech synthesis. Blind users and the mobile environment imply two strong constraints. First, pictures will be taken without control on camera settings and a priori information on text (font or size) and background. The second issue is to link several techniques together with an optimal compromise between computational constraints and recognition efficiency. We will present the overall description of the system from text detection to OCR error correction. This work aims at helping multimedia content understanding by deriving benefit from textual clues embedded in digital videos. For this, we developed a complete video Optical Character Recognition system (OCR), specifically adapted to detect and recognize embedded texts in videos. Based on a neural approach, this new method outperforms related work, especially in terms of robustness to style and size variabilities, to background complexity and to low resolution of the image. A language model that drives several steps of the video OCR is also introduced in order to remove ambiguities due to a local letter by letter recognition and to reduce segmentation errors. This approach has been evaluated on a database of French TV news videos and achieves an outstanding character recognition rate of 95 , corresponding to 78 of words correctly recognized, which enables its incorporation into an automatic video indexing and retrieval system.