Impact of online handwriting recognition performance on text categorization

Saldarriaga, Sebastián Peña; Viard-Gaudin, Christian; Morin, Emmanuel

doi:10.1007/s10032-009-0108-6

Cited by 2 publications

(2 citation statements)

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“…Recent works also analyze the impact of recognition errors (i.e. "noise") on recognition tasks [26], or on higher-level tasks such as text categorization [35].…”

Section: Handwriting Recognitionmentioning

confidence: 99%

“…In the field of noisy data processing, works have been focusing more particularly on dealing with text documents [39], such as blogs…, or transcripts from automatic speech recognition (ASR) systems [13,36]. However, there have been few works that deal with transcripts from handwriting recognition (HWR) systems [26,35]. The main issue with transcripts, whether they are from an ASR or a HWR system, is that they contain transcription errors.…”

Section: Introductionmentioning

confidence: 98%

See 1 more Smart Citation

Error handling approach using characterization and correction steps for handwritten document analysis

Quiniou

Cheriet

Anquetil

2011

IJDAR

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In this paper, we present a framework to handle recognition errors from a N -best list of output phrases given by a handwriting recognition system, with the aim to use the resulting phrases as inputs to a higher-level application. The framework can be decomposed into four main steps: phrase alignment, detection, characterization, and correction of word error hypotheses. First, the N -best phrases are aligned to the top-list phrase, and word posterior probabilities are computed and used as confidence indices to detect word error hypotheses on this top-list phrase (in comparison with a learned threshold). Then, the errors are characterized into predefined types, using the word posterior probabilities of the top-list phrase and other features to feed a trained SVM. Finally, the final output phrase is retrieved, thanks to a correction step that used the characterized error hypotheses and a designed word-to-class backoff language model. First experiments were conducted on the ImadocSen-OnDB handwritten sentence database and on the IAM-OnDB handwritten text database, using two recognizers. We present first results on an implementation of the proposed framework for handling recognition errors on transcripts of handwritten phrases provided by recognition systems.

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“…Recent works also analyze the impact of recognition errors (i.e. "noise") on recognition tasks [26], or on higher-level tasks such as text categorization [35].…”

Section: Handwriting Recognitionmentioning

confidence: 99%