Camera-based analysis of text and documents: a survey

Liang, Junbin; Doermann, David; Li, Huiping

doi:10.1007/s10032-004-0138-z

Cited by 367 publications

(163 citation statements)

References 86 publications

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“…It happens a lot if using a hand-held camera to take pictures. The effect is characters farther away look smaller and distorted, and parallel-line assumptions no longer hold in the image [16]. From our experience, small to mild perception distortion causes significant degradation in performance of our OCR engine.…”

Section: Perception Distortion (Tilt)mentioning

confidence: 87%

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OCRdroid: A Framework to Digitize Text Using Mobile Phones

Zhang

Joshi²,

Kadmawala³

et al. 2010

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract. As demand grows for mobile phone applications, research in optical character recognition, a technology well developed for scanned documents, is shifting focus to the recognition of text embedded in digital photographs. In this paper, we present OCRdroid, a generic framework for developing OCR-based applications on mobile phones. OCRdroid combines a light-weight image preprocessing suite installed inside the mobile phone and an OCR engine connected to a backend server. We demonstrate the power and functionality of this framework by implementing two applications called PocketPal and PocketReader based on OCRdroid on HTC Android G1 mobile phone. Initial evaluations of these pilot experiments demonstrate the potential of using OCRdroid framework for realworld OCR-based mobile applications.

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Section: Perception Distortion (Tilt)mentioning

confidence: 87%

“…However, camera-captured documents have some drawbacks. They suffer a lot from focus loss, uneven document lighting, and geometrical distortions, such as text skew, bad orientation, and text misalignment [16]. Moreover, since the system is running on a mobile phone, real time response is also a critical challenge.…”

Section: Introductionmentioning

confidence: 99%

OCRdroid: A Framework to Digitize Text Using Mobile Phones

Zhang

Joshi²,

Kadmawala³

et al. 2010

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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“…A significant body of work focuses on detecting text in natural scenes and video-frames. For comprehensive surveys of textdetection, see [8], [23]. In this work, we use a logistic regression classifier that uses a variety of text features, similar to the approaches described in [24] and [20].…”

Section: ) Detectionmentioning

confidence: 99%

“…The most challenging sub-problem in this system is the text detection and character recognition itself (a problem that has warranted much prior work [6], [7], [8], [9], [10]). Unfortunately, while some off-the-shelf components are available, their performance is generally low when applied to nondocument images (such as those acquired from our robot).…”

Section: Introductionmentioning

confidence: 99%

Autonomous sign reading for semantic mapping

Case

Suresh

Coates

et al. 2011

2011 IEEE International Conference on Robotics and Automation

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Abstract-We consider the problem of automatically collecting semantic labels during robotic mapping by extending the mapping system to include text detection and recognition modules. In particular, we describe a system by which a SLAMgenerated map of an office environment can be annotated with text labels such as room numbers and the names of office occupants. These labels are acquired automatically from signs posted on walls throughout a building. Deploying such a system using current text recognition systems, however, is difficult since even state-of-the-art systems have difficulty reading text from non-document images. Despite these difficulties we present a series of additions to the typical mapping pipeline that nevertheless allow us to create highly usable results. In fact, we show how our text detection and recognition system, combined with several other ingredients, allows us to generate an annotated map that enables our robot to recognize named locations specified by a user in 84% of cases.

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“…The effects of non-uniform illumination can be mitigating by taking calibration pictures. We can therefore expect that most document acquisition will soon take place with 2-D sensor arrays rather than linear sensor sweep [134,135].…”

Section: Cameras and Displaysmentioning

confidence: 99%

Adaptive and Interactive Approaches to Document Analysis

Nagy¹,

Veeramachaneni

2008

Machine Learning in Document Analysis and Recognition

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Summary. This chapter explores three aspects of learning in document analysis: (1) field classification, (2) interactive recognition, and (3) portable and networked applications. Context in document classification conventionally refers to language context, i.e., deterministic or statistical constraints on the sequence of letters in syllables or words, and on the sequence of words in phrases or sentences. We show how to exploit other types of statistical dependence, specifically the dependence between the shape features of several patterns due to the common source of the patterns within a field or a document. This type of dependence leads to field classification, where the features of some patterns may reveal useful information about the features of other patterns from the same source but not necessarily from the same class. We explore the relationship between field classification and the older concepts of unsupervised learning and adaptation. Human interaction is often more effective interspersed with algorithmic processes than only before or after the automated parts of the process. We develop a taxonomy for interaction during training and testing, and show how either human-initiated and machine-initiated interaction can lead to human and machine learning. In a section on new technologies, we discuss how new cameras and displays, web-wide access, interoperability, and essentially unlimited storage provide fertile new approaches to document analysis.

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Camera-based analysis of text and documents: a survey

Cited by 367 publications

References 86 publications

OCRdroid: A Framework to Digitize Text Using Mobile Phones

OCRdroid: A Framework to Digitize Text Using Mobile Phones

Autonomous sign reading for semantic mapping

Adaptive and Interactive Approaches to Document Analysis

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