Confidence Measures for Error Correction in Interactive Transcription Handwritten Text

Tarazón, Lionel; Pérez, Daniel; Serrano, Nicolás; Alabau, Vicent; Terrades, Oriol Ramos; Sanchís, Alberto; Juan, Alfons

doi:10.1007/978-3-642-04146-4_61

Cited by 10 publications

(9 citation statements)

References 8 publications

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“…These three limitations were overcome in previous work presented by the authors [21,23,24,27]. In our approach, we deal with the interactive transcription of handwritten text documents when user effort is limited.…”

Section: Computer Assisted Transcription Of Text Imagesmentioning

confidence: 99%

“…To this purpose, active learning techniques can be used [26]. In our approach, we adopt a strategy based on the use of confidence measures [19,31] in order to select which words should be supervised [27].…”

Section: Confidence Measuresmentioning

confidence: 99%

“…Confidence measures were tested in [27] on two real handwritten databases being GERMANA one of both. In this work, confidence measures are employed to automatically detect words to be supervised by a fictitious user knowing the reference transcription.…”

Section: Confidence Measuresmentioning

confidence: 99%

“…This paper provides a comprehensive description of models and techniques that have been studied and reported in separate works [21,23,24,27]. However, significantly improved baseline experimental results are reported for the first time in this work as a result of updating our feature extraction algorithm.…”

Section: Introductionmentioning

confidence: 98%

“…The aim is to build a system, which employs the limited user effort to generate the best possible transcriptions as efficiently as possible. The system employs the limited effort by supervising only hypothesised words that are likely to be misrecognised [27]. Thus, limited user effort is efficiently focused only on the supervision of the output parts for which the system is not confident enough.…”

Section: Introductionmentioning

confidence: 99%

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Interactive handwriting recognition with limited user effort

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Transcription of handwritten text in (old) documents is an important, time-consuming task for digital libraries. Although post-editing automatic recognition of handwritten text is feasible, it is not clearly better than simply ignoring it and transcribing the document from scratch. A more effective approach is to follow an interactive approach in which both, the system is guided by the user, and the user is assisted by the system to complete the transcription task as efficiently as possible. Nevertheless, in some applications, the user effort available to transcribe documents is limited and fully supervision of the system output is not realistic. To circumvent these problems, we propose a novel interactive approach which efficiently employs user effort to transcribe a document by improving three different aspects. Firstly, the system employs a limited amount of effort to solely supervise recognised words that are likely to be incorrect. Thus, user effort is efficiently focused on the supervision of words for which the system is not confident enough. Secondly, it refines the initial transcription provided to the user by recomputing it constrained to user supervisions. In this way, incorrect words in unsupervised parts can be automatically amended without user supervision. Finally, it improves the underlying system models by retraining the system from partially supervised transcriptions. In order to prove these statements, empirical results are presented on two real databases showing that the proposed approach can notably reduce user effort in the transcription of handwritten text in (old) documents.

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Section: Computer Assisted Transcription Of Text Imagesmentioning

confidence: 99%