a discriminative semi-markov model for robust …weinman/files/nescai08poster.pdfa discriminative...

A Discriminative Semi-Markov Model for Robust Scene Text RecognitionJerod J. Weinman, Erik Learned-Miller, and Allen R. Hanson

Computer Vision Laboratory, Dept. of Computer Science, University of Massachusetts Amherst

Problem and MotivationOur goal: Read text from images of the everyday world.

Applications include:•Aid to visually impaired•Photo annotation•Portable translation

Many new challenges are atypical of OCR page readers:•Small sample (<50 chars) •Perspective distortion

•Uneven lighting •Unusual fonts

•Word segmentation •Character segmentation

Previous work has assumed word boundaries, charactersegmentations, and/or known lexicon words. Theconditions above make these assumptions problematic.We propose a model that integrates lexical decision andboth word and character segmentation with recognition.

Semi-Markov Model

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We use a discriminatively trained semi-Markov model forrecognition. Like a CRF, it models dependencies betweenstates. It has the additional property of modeling theduration of a state, i.e., character.

Model parameterscan be learned fromlabeled training data.

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The exponent (U) is composed of functions that score asegmentation and labeling (y) of a text image (x) using:

•Appearance

•Bigrams

•Lexicon

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Parse ScoringA text image is parsed, or divided intolabeled segments. Parses may differ in thenumber of segments, and the states yi maydiffer in width.y1 y2 y3 y4

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AppearanceEvery possible parse segment isscored by a discriminant UA forcharacter appearance and width.

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BigramsEach pair of neighboring segments get a bigram score UB.

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LexiconA character invariant UL replaces UB in sequences forminglexicon words to promote known word recognition.

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Parse Gap/OverlapNeighboring states may have a gap or anoverlap, as in the case of ligatures. Theseare also scored using image features.

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Interpretation GraphAll possible parses can be represented by a weightedgraph. Dynamic programming can be used to find theoptimal path, or the best interpretation of the image.

ExperimentsTraining•Appearance: Synthetic images generated from 934 fonts•Bigrams: 82 books from Project Gutenberg•Lexicon: 50th frequency percentile words from SCOWL

Evaluation85 sign images of 1,144 characters at ~12 pixel x-height.

Char. Error RateNo Lex.

Forced Lex.Mixed

Allowing words both in and out ofthe lexicon reduces error by 13%.

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Above: Ourmodel reduceserror by 10%.

Left: Examples.

Low ResolutionIntegrated segmentation and recognition allow our modelto recognize images at lower resolutions than trained on.

End-to-End

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MultiscaleDetection

Text RegionCandidates

MultipleRecognitions

Top-ScoringOutput

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