1. Scanned Documents INST 734 Module 10 Doug Oard

2. Agenda Document image retrieval Representation  Retrieval Thanks for David Doermann for most of these slides

3. Character N-Grams Approach –Split up document into overlapping n-character sequences May or may not break on space between words –“DOCUMENT” -> DOC, OCU, CUM, UME, MEN, ENT Somewhat language-neutral –Use average word length to get a stemming-like effect Statistically robust to small numbers of errors –Good choice between 70%-85% character accuracy Above 85%, use words, dictionary-based correction, and stemming Below 70%, use shape codes –Modeling character confusion can ad a bit more robustness

4. Shape Codes Group all characters that have similar shapes –{A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R, S, T, U, V, W, X, Y, Z, 2, 3, 4, 5, 6, 7, 8, 9, 0} –{a, c, e, n, o, r, s, u, v, x, z} –{b, d, h, k, } –{f, t} –{g, p, q, y} –{i, j, l, 1} –{m, w} Faster than character recognition –Seconds per page, and very accurate Preserves recall, but with lower precision

5. Retrieval Using Image Features Advantages –Efficiency: process only what you need Particularly important for filtering tasks –Effectiveness: Limit effect of cascaded errors Applications –Handwriting Character segmentation is hard –Keyword spotting Capitalization of names is easily seen

6. Evaluation The simplest approach: Model-based evaluation –Apply character confusion statistics to an existing collection A bit better: Print-then-scan evaluation –Scanning is slow, but availability is no problem Best: Scan-only evaluation –Requires relevance judgments on document images

7. Key Points Converting images to text is a good start –But there is more to think about Image processing yields useful features Structure and content are both important –Always true for any collection –Especially clear with document images