Learning Surrogate Models of Document Image Quality Metrics for Automated Document Image Processing

Computation of document image quality metrics often depends upon the availability of a ground truth image corresponding to the document. This limits the applicability of quality metrics in applications such as hyperparameter optimization of image processing algorithms that operate on-the-fly on unseen documents. This work proposes the use of surrogate models to learn the behavior of a given document quality metric on existing datasets where ground truth images are available. The trained surrogate model can later be used to predict the metric value on previously unseen document images without requiring access to ground truth images. The surrogate model is empirically evaluated on the Document Image Binarization Competition (DIBCO) and the Handwritten Document Image Binarization Competition (H-DIBCO) datasets

A framework for applying natural language processing in digital health interventions

BACKGROUND: Digital health interventions (DHIs) are poised to reduce target symptoms in a scalable, affordable, and empirically supported way. DHIs that involve coaching or clinical support often collect text data from 2 sources: (1) open correspondence between users and the trained practitioners supporting them through a messaging system and (2) text data recorded during the intervention by users, such as diary entries. Natural language processing (NLP) offers methods for analyzing text, augmenting the understanding of intervention effects, and informing therapeutic decision making. OBJECTIVE: This study aimed to present a technical framework that supports the automated analysis of both types of text data often present in DHIs. This framework generates text features and helps to build statistical models to predict target variables, including user engagement, symptom change, and therapeutic outcomes. METHODS: We first discussed various NLP techniques and demonstrated how they are implemented in the presented framework. We then applied the framework in a case study of the Healthy Body Image Program, a Web-based intervention trial for eating disorders (EDs). A total of 372 participants who screened positive for an ED received a DHI aimed at reducing ED psychopathology (including binge eating and purging behaviors) and improving body image. These users generated 37,228 intervention text snippets and exchanged 4285 user-coach messages, which were analyzed using the proposed model. RESULTS: We applied the framework to predict binge eating behavior, resulting in an area under the curve between 0.57 (when applied to new users) and 0.72 (when applied to new symptom reports of known users). In addition, initial evidence indicated that specific text features predicted the therapeutic outcome of reducing ED symptoms. CONCLUSIONS: The case study demonstrates the usefulness of a structured approach to text data analytics. NLP techniques improve the prediction of symptom changes in DHIs. We present a technical framework that can be easily applied in other clinical trials and clinical presentations and encourage other groups to apply the framework in similar contexts

Bayes model for assessing the reading difficulty of English text for English education in Jordan

Predicting the reading difficulty level of English texts is a critical process for second language education and assessment. Reading difficulty level is concerned with the problem of matching a reader’s proficiency and the appropriate text. The reading difficulty level or readability assessment is the process for predicting the reading grade level required from an input text or document, which corresponds to the reader and to the materials. Students in Jordan at their academic levels find obstacles in finding relevant readable data for any subject at their levels. This paper is intended to introduce a model that foretells the reading difficulty level of a given text in terms of a student's ability to read and understand English as a non-native English speaker in Jordanian schools. In this paper, Jordanian students were classified into four categories according to their knowledge of English. The prediction of the reading difficulty level is achieved by using a modern statistical model that is situated on the Bayes model. The model compares the given text with some standard predefined text that strongly reflects the ability to read and understand English text. The accuracy of the proposed model was tested using the hold-out method. The overall prediction accuracy was 75.9%

Character-based Automated Human Perception Quality Assessment In Document Images

Large degradations in document images impede their readability and deteriorate the performance of automated document processing systems. Document image quality (IQ) metrics have been defined through optical character recognition (OCR) accuracy. Such metrics, however, do not always correlate with human perception of IQ. When enhancing document images with the goal of improving readability, e.g., in historical documents where OCR performance is low and/or where it is necessary to preserve the original context, it is important to understand human perception of quality. The goal of this paper is to design a system that enables the learning and estimation of human perception of document IQ. Such a metric can be used to compare existing document enhancement methods and guide automated document enhancement. Moreover, the proposed methodology is designed as a general framework that can be applied in a wide range of applications. © 2012 IEEE

Summarising News Stories for Children

This paper proposes a system to automatically summarise news articles in a manner suitable for children by deriving and combining statistical ratings for how important, positively oriented and easy to read each sentence is. Our results demonstrate that this approach succeeds in generating summaries that are suitable for children, and that there is further scope for combining this extractive approach with abstractive methods used in text implification

Role of images on World Wide Web readability

As the Internet and World Wide Web have grown, many good things have come. If you have access to a computer, you can find a lot of information quickly and easily. Electronic devices can store and retrieve vast amounts of data in seconds. You no longer have to leave your house to get products and services you could only get in person. Documents can be changed from English to Urdu or from text to speech almost instantly, making it easy for people from different cultures and with different abilities to talk to each other. As technology improves, web developers and website visitors want more animation, colour, and technology. As computers get faster at processing images and other graphics, web developers use them more and more. Users who can see colour, pictures, animation, and images can help understand and read the Web and improve the Web experience. People who have trouble reading or whose first language is not used on the website can also benefit from using pictures. But not all images help people understand and read the text they go with. For example, images just for decoration or picked by the people who made the website should not be used. Also, different factors could affect how easy it is to read graphical content, such as a low image resolution, a bad aspect ratio, a bad colour combination in the image itself, a small font size, etc., and the WCAG gave different rules for each of these problems. The rules suggest using alternative text, the right combination of colours, low contrast, and a higher resolution. But one of the biggest problems is that images that don't go with the text on a web page can make it hard to read the text. On the other hand, relevant pictures could make the page easier to read. A method has been suggested to figure out how relevant the images on websites are from the point of view of web readability. This method combines different ways to get information from images by using Cloud Vision API and Optical Character Recognition (OCR), and reading text from websites to find relevancy between them. Techniques for preprocessing data have been used on the information that has been extracted. Natural Language Processing (NLP) technique has been used to determine what images and text on a web page have to do with each other. This tool looks at fifty educational websites' pictures and assesses their relevance. Results show that images that have nothing to do with the page's content and images that aren't very good cause lower relevancy scores. A user study was done to evaluate the hypothesis that the relevant images could enhance web readability based on two evaluations: the evaluation of the 1024 end users of the page and the heuristic evaluation, which was done by 32 experts in accessibility. The user study was done with questions about what the user knows, how they feel, and what they can do. The results back up the idea that images that are relevant to the page make it easier to read. This method will help web designers make pages easier to read by looking at only the essential parts of a page and not relying on their judgment.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: José Luis Lépez Cuadrado.- Secretario: Divakar Yadav.- Vocal: Arti Jai