Topic
Optical character recognition
About: Optical character recognition is a research topic. Over the lifetime, 7342 publications have been published within this topic receiving 158193 citations. The topic is also known as: OCR & optical character reader.
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TL;DR: This work addresses two problems that are often encountered in object recognition: object segmentation, for which a distance sets shape filter is formulated, and shape matching, which is illustrated on printed and handwritten character recognition and detection of traffic signs in complex scenes.
Abstract: We introduce a novel rich local descriptor of an image point, we call the (labeled) distance set, which is determined by the spatial arrangement of image features around that point. We describe a two-dimensional (2D) visual object by the set of (labeled) distance sets associated with the feature points of that object. Based on a dissimilarity measure between (labeled) distance sets and a dissimilarity measure between sets of (labeled) distance sets, we address two problems that are often encountered in object recognition: object segmentation, for which we formulate a distance sets shape filter, and shape matching. The use of the shape filter is illustrated on printed and handwritten character recognition and detection of traffic signs in complex scenes. The shape comparison procedure is illustrated on handwritten character classification, COIL-20 database object recognition and MPEG-7 silhouette database retrieval.
256 citations
19 Jul 2018
TL;DR: Rosetta as mentioned in this paper is a scalable optical character recognition (OCR) system designed to process images uploaded daily at Facebook scale, which can detect and recognize text in images uploaded to Facebook.
Abstract: In this paper we present a deployed, scalable optical character recognition (OCR) system, which we call Rosetta , designed to process images uploaded daily at Facebook scale. Sharing of image content has become one of the primary ways to communicate information among internet users within social networks such as Facebook, and the understanding of such media, including its textual information, is of paramount importance to facilitate search and recommendation applications. We present modeling techniques for efficient detection and recognition of text in images and describe Rosetta 's system architecture. We perform extensive evaluation of presented technologies, explain useful practical approaches to build an OCR system at scale, and provide insightful intuitions as to why and how certain components work based on the lessons learnt during the development and deployment of the system.
253 citations
18 Jun 2018
TL;DR: The arbitrary orientation network (AON) is developed to directly capture the deep features of irregular texts, which are combined into an attention-based decoder to generate character sequence and is comparable to major existing methods in regular datasets.
Abstract: Recognizing text from natural images is a hot research topic in computer vision due to its various applications. Despite the enduring research of several decades on optical character recognition (OCR), recognizing texts from natural images is still a challenging task. This is because scene texts are often in irregular (e.g. curved, arbitrarily-oriented or seriously distorted) arrangements, which have not yet been well addressed in the literature. Existing methods on text recognition mainly work with regular (horizontal and frontal) texts and cannot be trivially generalized to handle irregular texts. In this paper, we develop the arbitrary orientation network (AON) to directly capture the deep features of irregular texts, which are combined into an attention-based decoder to generate character sequence. The whole network can be trained end-to-end by using only images and word-level annotations. Extensive experiments on various benchmarks, including the CUTE80, SVT-Perspective, IIIT5k, SVT and ICDAR datasets, show that the proposed AON-based method achieves the-state-of-the-art performance in irregular datasets, and is comparable to major existing methods in regular datasets.
252 citations
01 Dec 2007
TL;DR: In this paper, the authors used simple pattern recognition algorithms but exploited fatal design errors that were discovered in each CAPTCHA scheme and showed that their simple attacks can also break many other schemes deployed on the Internet at the time of writing.
Abstract: Visual CAPTCHAs have been widely used across the Internet to defend against undesirable or malicious bot programs. In this paper, we document how we have broken most such visual schemes provided at Captchaservice.org, a publicly available web service for CAPTCHA generation. These schemes were effectively resistant to attacks conducted using a high-quality Optical Character Recognition program, but were broken with a near 100% success rate by our novel attacks. In contrast to early work that relied on sophisticated computer vision or machine learning algorithms, we used simple pattern recognition algorithms but exploited fatal design errors that we discovered in each scheme. Surprisingly, our simple attacks can also break many other schemes deployed on the Internet at the time of writing: their design had similar errors. We also discuss defence against our attacks and new insights on the design of visual CAPTCHA schemes.
250 citations
TL;DR: A pattern recognition system which works with the mechanism of the neocognitron, a neural network model for deformation-invariant visual pattern recognition, is discussed, which has been trained to recognize 35 handwritten alphanumeric characters.
Abstract: A pattern recognition system which works with the mechanism of the neocognitron, a neural network model for deformation-invariant visual pattern recognition, is discussed. The neocognition was developed by Fukushima (1980). The system has been trained to recognize 35 handwritten alphanumeric characters. The ability to recognize deformed characters correctly depends strongly on the choice of the training pattern set. Some techniques for selecting training patterns useful for deformation-invariant recognition of a large number of characters are suggested. >
249 citations