Cell phones and other portable devices are equipped with a variety of technologies by which existing functionality can be improved, and new functionality can be provided. Some relate to visual search capabilities, and determining appropriate actions responsive to different image inputs. Others relate to processing of image data. Still others concern metadata generation, processing, and representation. Yet others relate to coping with fixed focus limitations of cell phone cameras, e.g., in reading digital watermark data. Still others concern user interface improvements. A great number of other features and arrangements are also detailed.

Methods and Systems for Content Processing

We present a novel image operator that seeks to find the value of stroke width for each image pixel, and demonstrate its use on the task of text detection in natural images. The suggested operator is local and data dependent, which makes it fast and robust enough to eliminate the need for multi-scale computation or scanning windows. Extensive testing shows that the suggested scheme outperforms the latest published algorithms. Its simplicity allows the algorithm to detect texts in many fonts and languages.

/pdf/detecting-text-in-natural-scenes-with-stroke-width-transform-1p4jkf3lrz.pdf

Detecting text in natural scenes with stroke width transform

tions. Bootstrap has found many applications in engineering field, including artificial neural networks, biomedical engineering, environmental engineering, image processing, and radar and sonar signal processing. Basic concepts of the bootstrap are summarized in each section as a step-by-step algorithm for ease of implementation. Most of the applications are taken from the signal processing literature. The principles of the bootstrap are introduced in Chapter 2. Both the nonparametric and parametric bootstrap procedures are explained. Babu and Singh (1984) have demonstrated that in general, these two procedures behave similarly for pivotal (Studentized) statistics. The fact that the bootstrap is not the solution for all of the problems has been known to statistics community for a long time; however, this fact is rarely touched on in the manuscripts meant for practitioners. It was first observed by Babu (1984) that the bootstrap does not work in the infinite variance case. Bootstrap Techniques for Signal Processing explains the limitations of bootstrap method with an example. I especially liked the presentation style. The basic results are stated without proofs; however, the application of each result is presented as a simple step-by-step process, easy for nonstatisticians to follow. The bootstrap procedures, such as moving block bootstrap for dependent data, along with applications to autoregressive models and for estimation of power spectral density, are also presented in Chapter 2. Signal detection in the presence of noise is generally formulated as a testing of hypothesis problem. Chapter 3 introduces principles of bootstrap hypothesis testing. The topics are introduced with interesting real life examples. Flow charts, typical in engineering literature, are used to aid explanations of the bootstrap hypothesis testing procedures. The bootstrap leads to second-order correction due to pivoting; this improvement in the results due to pivoting is also explained. In the second part of Chapter 3, signal processing is treated as a regression problem. The performance of the bootstrap for matched filters as well as constant false-alarm rate matched filters is also illustrated. Chapters 2 and 3 focus on estimation problems. Chapter 4 introduces bootstrap methods used in model selection. Due to the inherent structure of the subject matter, this chapter may be difficult for nonstatisticians to follow. Chapter 5 is the most impressive chapter in the book, especially from the standpoint of statisticians. It provides real data bootstrap applications to illustrate the theory covered in the earlier chapters. These include applications to optimal sensor placement for knock detection and land-mine detection. The authors also provide a MATLAB toolbox comprising frequently used routines. Overall, this is a very useful handbook for engineers, especially those working in signal processing.

Probability and Random Processes

https://www.comp.nus.edu.sg/~cs4243/projects/TextDetectionSurvey.pdf

Text information extraction in images and video: a survey

With the increasing popularity of practical vision systems and smart phones, text detection in natural scenes becomes a critical yet challenging task. Most existing methods have focused on detecting horizontal or near-horizontal texts. In this paper, we propose a system which detects texts of arbitrary orientations in natural images. Our algorithm is equipped with a two-level classification scheme and two sets of features specially designed for capturing both the intrinsic characteristics of texts. To better evaluate our algorithm and compare it with other competing algorithms, we generate a new dataset, which includes various texts in diverse real-world scenarios; we also propose a protocol for performance evaluation. Experiments on benchmark datasets and the proposed dataset demonstrate that our algorithm compares favorably with the state-of-the-art algorithms when handling horizontal texts and achieves significantly enhanced performance on texts of arbitrary orientations in complex natural scenes.

/pdf/detecting-texts-of-arbitrary-orientations-in-natural-images-4zksfhwvgm.pdf

Detecting texts of arbitrary orientations in natural images

Text that appears in a scene or is graphically added to video can provide an important supplemental source of index information as well as clues for decoding the video's structure and for classification. In this work, we present algorithms for detecting and tracking text in digital video. Our system implements a scale-space feature extractor that feeds an artificial neural processor to detect text blocks. Our text tracking scheme consists of two modules: a sum of squared difference (SSD) based module to find the initial position and a contour-based module to refine the position. Experiments conducted with a variety of video sources show that our scheme can detect and track text robustly.

Automatic text detection and tracking in digital video

The increasing availability of high-performance, low-priced, portable digital imaging devices has created a tremendous opportunity for supplementing traditional scanning for document image acquisition. Digital cameras attached to cellular phones, PDAs, or wearable computers, and standalone image or video devices are highly mobile and easy to use; they can capture images of thick books, historical manuscripts too fragile to touch, and text in scenes, making them much more versatile than desktop scanners. Should robust solutions to the analysis of documents captured with such devices become available, there will clearly be a demand in many domains. Traditional scanner-based document analysis techniques provide us with a good reference and starting point, but they cannot be used directly on camera-captured images. Camera-captured images can suffer from low resolution, blur, and perspective distortion, as well as complex layout and interaction of the content and background. In this paper we present a survey of application domains, technical challenges, and solutions for the analysis of documents captured by digital cameras. We begin by describing typical imaging devices and the imaging process. We discuss document analysis from a single camera-captured image as well as multiple frames and highlight some sample applications under development and feasible ideas for future development.

Camera-based analysis of text and documents: a survey

The increasing availability of high performance, low priced, portable digital imaging devices has created a tremendous opportunity for supplementing traditional scanning for document image acquisition. Digital cameras attached to cellular phones, PDAs, or as standalone still or video devices are highly mobile and easy to use; they can capture images of any kind of document including very thick books, historical pages too fragile to touch, and text in scenes; and they are much more versatile than desktop scanners. Should robust solutions to the analysis of documents captured with such devices become available, there is clearly a demand from many domains. Traditional scanner-based document analysis techniques provide us with a good reference and starting point, but they cannot be used directly on camera-captured images. Camera captured images can suffer from low resolution, blur, and perspective distortion, as well as complex layout and interaction of the content and background. In this paper we present a survey of application domains, technical challenges and solutions for recognizing documents captured by digital cameras. We begin by describing typical imaging devices and the imaging process. We discuss document analysis from a single camera-captured image as well as multiple frames and highlight some sample applications under development and feasible ideas for future development.

Progress in camera-based document image analysis

In this paper, we address the problem of the identification of text in noisy document images. We are especially focused on segmenting and identifying between handwriting and machine printed text because: 1) Handwriting in a document often indicates corrections, additions, or other supplemental information that should be treated differently from the main content and 2) the segmentation and recognition techniques requested for machine printed and handwritten text are significantly different. A novel aspect of our approach is that we treat noise as a separate class and model noise based on selected features. Trained Fisher classifiers are used to identify machine printed text and handwriting from noise and we further exploit context to refine the classification. A Markov Random Field-based (MRF) approach is used to model the geometrical structure of the printed text, handwriting, and noise to rectify misclassifications. Experimental results show that our approach is robust and can significantly improve page segmentation in noisy document collections.

/pdf/machine-printed-text-and-handwriting-identification-in-noisy-sd3e5xo6r7.pdf

Machine printed text and handwriting identification in noisy document images

The continuous development of object detection algorithms is ushering in the need for evaluation tools to quantify algorithm performance. In this paper a set of seven metrics are proposed for quantifying different aspects of a detection algorithm's performance. The strengths and weaknesses of these metrics are described. They are implemented in the Video Performance Evaluation Resource (ViPER) system and will be used to evaluate algorithms for detecting text, faces, moving people and vehicles. Results for running two previous text-detection algorithms on a common data set are presented.

Huiping Li

Papers

Automatic text detection and tracking in digital video

Camera-based analysis of text and documents: a survey

Progress in camera-based document image analysis

Machine printed text and handwriting identification in noisy document images

Performance evaluation of object detection algorithms