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Noisy text analytics

About: Noisy text analytics is a research topic. Over the lifetime, 700 publications have been published within this topic receiving 28759 citations.


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Journal Article
Shi Bing1
TL;DR: Text categorization-assignment of natural language texts to one or more predefined categories based on their content-is an important component in many information organization and management tasks.
Abstract: Text categorization-assignment of natural language texts to one or more predefined categories based on their content-is an important component in many information organization and management tasks.Different automatic learning algorithms for text categori-zation have different classification accuracy.Very accurate text classifiers can be learned automatically from training examples.

384 citations

Posted Content
TL;DR: The COCO-Text dataset is described, which contains over 173k text annotations in over 63k images and presents an analysis of three leading state-of-the-art photo Optical Character Recognition (OCR) approaches on the dataset.
Abstract: This paper describes the COCO-Text dataset. In recent years large-scale datasets like SUN and Imagenet drove the advancement of scene understanding and object recognition. The goal of COCO-Text is to advance state-of-the-art in text detection and recognition in natural images. The dataset is based on the MS COCO dataset, which contains images of complex everyday scenes. The images were not collected with text in mind and thus contain a broad variety of text instances. To reflect the diversity of text in natural scenes, we annotate text with (a) location in terms of a bounding box, (b) fine-grained classification into machine printed text and handwritten text, (c) classification into legible and illegible text, (d) script of the text and (e) transcriptions of legible text. The dataset contains over 173k text annotations in over 63k images. We provide a statistical analysis of the accuracy of our annotations. In addition, we present an analysis of three leading state-of-the-art photo Optical Character Recognition (OCR) approaches on our dataset. While scene text detection and recognition enjoys strong advances in recent years, we identify significant shortcomings motivating future work.

361 citations

Proceedings ArticleDOI
16 Jun 2012
TL;DR: This work presents a framework that exploits both bottom-up and top-down cues in the problem of recognizing text extracted from street images, and shows significant improvements in accuracies on two challenging public datasets, namely Street View Text and ICDAR 2003.
Abstract: 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%).

349 citations

Proceedings ArticleDOI
06 Jan 2014
TL;DR: This work developed a prototypical system called the Word Cloud Explorer that relies entirely on word clouds as a visualization method and shows how this approach can be effectively used to solve text analysis tasks and evaluated in a qualitative user study.
Abstract: Word clouds have emerged as a straightforward and visually appealing visualization method for text. They are used in various contexts as a means to provide an overview by distilling text down to those words that appear with highest frequency. Typically, this is done in a static way as pure text summarization. We think, however, that there is a larger potential to this simple yet powerful visualization paradigm in text analytics. In this work, we explore the usefulness of word clouds for general text analysis tasks. We developed a prototypical system called the Word Cloud Explorer that relies entirely on word clouds as a visualization method. It equips them with advanced natural language processing, sophisticated interaction techniques, and context information. We show how this approach can be effectively used to solve text analysis tasks and evaluate it in a qualitative user study.

318 citations

Proceedings ArticleDOI
23 Jun 2014
TL;DR: This paper proposes a novel multi-scale representation for scene text recognition that consists of a set of detectable primitives, termed as strokelets, which capture the essential substructures of characters at different granularities.
Abstract: Driven by the wide range of applications, scene text detection and recognition have become active research topics in computer vision. Though extensively studied, localizing and reading text in uncontrolled environments remain extremely challenging, due to various interference factors. In this paper, we propose a novel multi-scale representation for scene text recognition. This representation consists of a set of detectable primitives, termed as strokelets, which capture the essential substructures of characters at different granularities. Strokelets possess four distinctive advantages: (1) Usability: automatically learned from bounding box labels, (2) Robustness: insensitive to interference factors, (3) Generality: applicable to variant languages, and (4) Expressivity: effective at describing characters. Extensive experiments on standard benchmarks verify the advantages of strokelets and demonstrate the effectiveness of the proposed algorithm for text recognition.

303 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20236
20228
20201
20191
20184
201723