Preface. Chapter 1. The Evolution of Survey Process Quality. 1.1 The Concept of a Survey. 1.2 Types of Surveys. 1.3 Brief History of Survey Methodology. 1.4 The Quality Revolution. 1.5 Definitions of Quality and Quality in Statistical Organizations. 1.6 Measuring Quality. 1.7 Improving Quality. 1.8 Quality in a Nutshell. Chapter 2. The Survey Process and Data Quality. 2.1 Overview of the Survey Process. 2.2 Data Quality and Total Survey Error. 2.3 Decomposing Nonsampling Error into Its Component Parts. 2.4 Gauging the Magnitude of Total Survey Error. 2.5 Mean Squared Error. 2.6 An Illustration of the Concepts. Chapter 3. Coverage and Nonresponse Error. 3.1 Coverage Error. 3.2 Measures of Coverage Bias. 3.3 Reducing Coverage Bias. 3.4 Unit Nonresponse Error. 3.5 Calculating Response Rates. 3.6 Reducing Nonresponse Bias. Chapter 4. The Measurement Process and Its Implications for Questionnaire Design. 4.1Components of Measurement Error. 4.2 Errors Arising from the Questionnaire Design. 4.3 Understanding the Response Process. Chapter 5. Errors Due to Interviewers and Interviewing. 5.1 Role of the Interviewer. 5.2 Interviewer Variability. 5.3 Design Factors that Influence Interviewer Effects. 5.4 Evaluation of Interviewer Performance. Chapter 6. Data Collection Modes and Associated Errors. 6.1 Modes of Data Collection. 6.2 Decision Regarding Mode. 6.3 Some Examples of Mode Effects. Chapter 7. Data Processing: Errors and Their Control. 7.1 Overview of Data Processing Steps. 7.2 Nature of Data Processing Error. 7.3 Data Capture Errors. 7.4 Post-Data Capture Editing. 7.5 Coding. 7.6 File Preparation. 7.7 Applications of Continuous Quality Improvement: The Case of Coding. 7.8 Integration Activities. Chapter 8. Overview of Survey Error Evaluation Methods. 8.1 Purposes of Survey Error Evaluation. 8.2 Evaluation Methods for Designing and Pretesting Surveys. 8.3 Methods for Monitoring and Controlling Data Quality. 8.4 Postsurvey Evaluations. 8.5 Summary of Evaluation Methods. Chapter 9. Sampling Error. 9.1 Brief History of Sampling. 9.2 Nonrandom Sampling Methods. 9.3 Simple Random Sampling. 9.4 Statistical Inference in the Presence of Nonsampling Errors. 9.5 Other Methods of Random Sampling. 9.6 Concluding Remarks. Chapter 10.1 Practical Survey Design for Minimizing Total Survey Error. 10.1 Balance Between Cost, Survey Error, and Other Quality Features. 10.2 Planning a Survey for Optimal Quality. 10.3 Documenting Survey Quality. 10.4 Organizational Issues Related to Survey Quality. References. Index.

Introduction to Survey Quality

We describe a method of recognizing handwritten digits by fitting generative models that are built from deformable B-splines with Gaussian "ink generators" spaced along the length of the spline. The splines are adjusted using a novel elastic matching procedure based on the expectation maximization algorithm that maximizes the likelihood of the model generating the data. This approach has many advantages: 1) the system not only produces a classification of the digit but also a rich description of the instantiation parameters which can yield information such as the writing style; 2) the generative models can perform recognition driven segmentation; 3) the method involves a relatively small number of parameters and hence training is relatively easy and fast; and 4) unlike many other recognition schemes, it does not rely on some form of pre-normalization of input images, but can handle arbitrary scalings, translations and a limited degree of image rotation. We have demonstrated that our method of fitting models to images does not get trapped in poor local minima. The main disadvantage of the method is that it requires much more computation than more standard OCR techniques.

/pdf/using-generative-models-for-handwritten-digit-recognition-3kzcl6ultm.pdf

Using generative models for handwritten digit recognition

Handwritten numeral recognition using gradient and curvature of gray scale image

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Subspace Classifiers in Recognition of Handwritten Digits

The Second Census Optical Character Recognition Systems Conference

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The Second Census Optical Character Recognition Systems Conference