Analysis of whole-book recognition

2010 20th International Conference on Pattern Recognition

2010

Self Cite

Whole-book recognition is a document image analysis strategy that operates on the complete set of a book's page images using automatic adaptation to improve accuracy. Our algorithm expects to be given approximate iconic and linguistic models-derived from (generally errorful) OCR results and (generally incomplete) dictionaries-and then, guided entirely by evidence internal to the test set, corrects the models yielding improved accuracy. The iconic model describes image formation and determines the behavior of a character-image classifier. The linguistic model describes word-occurrence probabilities. In previous work, we reported that adapting the iconic model alone (with a perfect linguistic model) was able to automatically reduce word error rate on a 180-page book by a large factor. In this paper, we propose an algorithm that adapts both the iconic model and the linguistic model alternately to improve both models on the fly. The linguistic model adaptation method, which we report here, identifies new words and adds them to the dictionary. With 64.6% words missing in the initial dictionary, our previous algorithm reduced word error rate from 40.2% to 23.2%. The new algorithm drives word error rate down further from 23.2% to 16.0%.

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Incorporating Linguistic Model Adaptation into Whole-Book Recognition

Xiu

2010 20th International Conference on Pattern Recognition

2010

Self Cite

“…Pingping Xiu and I, believing that model adaptation operating on long passages might shed light on these questions, explored "whole-book" recognition [57], a strategy that operates on the complete set of a book's page images using automatic adaptation to improve accuracy.…”

Section: B Mutually Correcting Modelsmentioning

confidence: 99%

Document Recognition without Strong Models

2011 International Conference on Document Analysis and Recognition

2011

Self Cite

Abstract-Can a high-performance document image recognition system be built without detailed knowledge of the application? Having benefited from the statistical machinelearning revolution of the last twenty years, our architectures rely less on hand-crafted special-case rules and more on models trained on labeled-sample data sets. But urgent questions remain. When we can't collect (and label) enough real training data, does it help to complement them with data synthesized using generative models? Is it ever completely safe to rely on synthetic data? If we can't manage to train (or craft) a single complete, near-perfect, application-specific "strong" model to drive recognition, can we make progress by combining several imperfect or incomplete "weak" models? Can recognition that is carried out jointly over weak models perform optimally while still running fast? Can a recognizer automatically pick a strong model of its input? Must we always pre-train models for every kind ("style") of input expected, or can a recognizer adapt to unknown styles? Can weak models adapt autonomously, growing stronger and so driving accuracy higher, without any human intervention? Can one model "criticize"-and then proceed to correct-other models, even while it is being criticized and corrected in turn by them? After twenty-five years of research on these questions we have partial answers, many in the affirmative: in addition to promising laboratory demonstrations, we can take pride in successful applications. I'll illustrate the evolution of the state of the art with concrete examples, and point out open problems.(Based on work by and with T. Pavlidis, T.

Whole-Book Recognition

Xiu

IEEE Trans. Pattern Anal. Mach. Intell.

2012

Whole-book recognition is a document image analysis strategy that operates on the complete set of a book's page images using automatic adaptation to improve accuracy. We describe an algorithm which expects to be initialized with approximate iconic and linguistic models--derived from (generally errorful) OCR results and (generally imperfect) dictionaries--and then, guided entirely by evidence internal to the test set, corrects the models which, in turn, yields higher recognition accuracy. The iconic model describes image formation and determines the behavior of a character-image classifier, and the linguistic model describes word-occurrence probabilities. Our algorithm detects "disagreements" between these two models by measuring cross entropy between 1) the posterior probability distribution of character classes (the recognition results resulting from image classification alone) and 2) the posterior probability distribution of word classes (the recognition results from image classification combined with linguistic constraints). We show how disagreements can identify candidates for model corrections at both the character and word levels. Some model corrections will reduce the error rate over the whole book, and these can be identified by comparing model disagreements, summed across the whole book, before and after the correction is applied. Experiments on passages up to 180 pages long show that when a candidate model adaptation reduces whole-book disagreement, it is also likely to correct recognition errors. Also, the longer the passage operated on by the algorithm, the more reliable this adaptation policy becomes, and the lower the error rate achieved. The best results occur when both the iconic and linguistic models mutually correct one another. We have observed recognition error rates driven down by nearly an order of magnitude fully automatically without supervision (or indeed without any user intervention or interaction). Improvement is nearly monotonic, and asymptotic accuracy is stable, even over long runs. If implemented naively, the algorithm runs in time quadratic in the length of the book, but random subsampling and caching techniques speed it up by two orders of magnitude with negligible loss of accuracy. Whole-book recognition has potential applications in digital libraries as a safe unsupervised anytime algorithm.