Reading (process)

Abstract: Recent studies of eye movements in reading and other information processing tasks, such as music reading, typing, visual search, and scene perception, are reviewed. The major emphasis of the review is on reading as a specific example of cognitive processing. Basic topics discussed with respect to reading are (a) the characteristics of eye movements, (b) the perceptual span, (c) integration of information across saccades, (d) eye movement control, and (e) individual differences (including dyslexia). Similar topics are discussed with respect to the other tasks examined. The basic theme of the review is that eye movement data reflect moment-to-moment cognitive processes in the various tasks examined. Theoretical and practical considerations concerning the use of eye movement data are also discussed.

Abstract: Contents: Preface. Part I: R. Barr, Society and Literacy. S.B. Heath, The Sense of Being Literate: Historical and Cross-Cultural Features. P. Foster, A. Purves, Literacy and Society with Particular Reference to the Non-Western World. R.L. Venezky, The Development of Literacy in the Industrialized Nations of the West. J.T. Guthrie, V. Greaney, Literacy Acts. R-M. Weber, Linguistic Diversity and Reading in American Society. J.S. Chall, J.R. Squire, The Publishing Industry and Textbooks. P. Shannon, Politics, Policy, and Reading Research. Part II: M.L. Kamil, Task and Format Variables in Reading Research. A. Graesser, J.M. Golding, D.L. Long, Narrative Representation and Comprehension. S. Bovair, D.E. Kieras, Toward a Model of Acquiring Procedures from Text. C.A. Weaver, III, W. Kintsch, Expository Text. R.J. Tierney, T. Shanahan, Research on the Reading-Writing Relationship: Interactions, Transactions, and Outcomes. R. Calfee, E. Hiebert, Classroom Assessment of Reading. D. Reinking, L. Bridwell-Bowles, Computers in Reading and Writing. R. Waller, Typography and Discourse. Part III: P.B. Mosenthal, Constructs of Reader Process. L.C. Ehri, Development of the Ability to Read Words. K.E. Stanovich, Word Recognition: Changing Perspectives. R. Beach, S. Hynds, Research on Response to Literature. T.P. McNamara, D.L. Miller, J.D. Bransford, Mental Models and Reading Comprehension. M. Daneman, Individual Differences in Reading Skills. K.K. Wixson, M.Y. Lipson, Perspectives on Reading Disability Research. F.R. Vellutino, M.B. Denckla, Cognitive and Neuropsychological Foundations of Word Identification in Poor and Normally Developing Readers. S.G. Paris, B.A. Wasik, J.C. Turner, The Development of Strategic Readers. M. Hegarty, P.A. Carpenter, M.A. Just, Diagrams in the Comprehension of Scientific Texts. L. Mikulecky, R. Drew, Basic Literacy Skills in the Workplace. R.C. Anderson, W.E. Nagy, Word Meanings. Part IV: P.D. Pearson, Literacy and Schooling. E. Sulzby, W. Teale, Emergent Literacy. C. Juel, Beginning Reading. I. Beck, M. McKeown, Conditions of Vocabulary Acquisition. P.D. Pearson, L. Fielding, Comprehension Instruction. L.R. Roehler, G.G. Duffy, Teachers' Instructional Actions. R. Barr, R. Dreeben, Grouping Students for Reading Instruction. J.V. Hoffman, Teacher and School Effects in Learning to Read. D.E. Alvermann, D.W. Moore, Secondary School Reading. P. Johnston, R. Allington, Remediation. Epilogue: P.B. Mosenthal, M.L. Kamil, Understanding Progress in Reading Research.

Abstract: Detecting and reading text from natural images is a hard computer vision task that is central to a variety of emerging applications. Related problems like document character recognition have been widely studied by computer vision and machine learning researchers and are virtually solved for practical applications like reading handwritten digits. Reliably recognizing characters in more complex scenes like photographs, however, is far more difficult: the best existing methods lag well behind human performance on the same tasks. In this paper we attack the problem of recognizing digits in a real application using unsupervised feature learning methods: reading house numbers from street level photos. To this end, we introduce a new benchmark dataset for research use containing over 600,000 labeled digits cropped from Street View images. We then demonstrate the difficulty of recognizing these digits when the problem is approached with hand-designed features. Finally, we employ variants of two recently proposed unsupervised feature learning methods and find that they are convincingly superior on our benchmarks.