Language Identification for Interactive Handwriting Transcription of Multilingual Documents

Agua, Miguel A. del; Serrano, Nicolás; Juan, Alfons

doi:10.1007/978-3-642-21257-4_74

Cited by 2 publications

(11 citation statements)

References 6 publications

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“…Preliminary results exploiting multilinguality on the GERMANA database proved the benefits of explicitly modelling language identification at the line level in a interactive transcripcion scenario [7]. However, these results are far from allowing an effective interactive transcription.…”

Section: Previous Workmentioning

confidence: 99%

“…However, conventional (non-interactive) script and language identification are still in its early stage of research [8], and have remained unexplored until very recently [7].…”

Section: Previous Workmentioning

confidence: 99%

“…A series of n-gram language identification models were proposed in [7]. In this work, we applied the best performing models, the unigram model…”

Section: Probabilistic Frameworkmentioning

confidence: 99%

“…Another distinctive feature of GERMANA is the large number of out-of-vocabulary (OOV) words accentuated by its multilingual nature. This feature has been the main reason for the relatively poor results obtained so far on the GERMANA database [7].…”

Section: Introductionmentioning

confidence: 99%

“…Multilinguality is captured by language identification models already discussed in [7]. The problem of OOV words is tackled by deploying character-based n-gram language models.…”

Section: Introductionmentioning

confidence: 99%

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Character-Based Handwritten Text Recognition of Multilingual Documents

Agua

Serrano

Civera

et al. 2012

Advances in Speech and Language Technologies for Iberian Languages

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Abstract. An effective approach to transcribe handwritten text documents is to follow a sequential interactive approach. During the supervision phase, user corrections are incorporated into the system through an ongoing retraining process. In the case of multilingual documents with a high percentage of out-of-vocabulary (OOV) words, two principal issues arise. On the one hand, a minor yet important matter for this interactive approach is to identify the language of the current text line image to be transcribed, as a language dependent recognisers typically performs better than a monolingual recogniser. On the other hand, word-based language models suffer from data scarcity in the presence of a large number of OOV words, degrading their estimation and affecting the performance of the transcription system. In this paper, we successfully tackle both issues deploying character-based language models combined with language identification techniques on an entire 764-page multilingual document. The results obtained significantly reduce previously reported results in terms of transcription error on the same task, but showed that a language dependent approach is not effective on top of character-based recognition of similar languages.

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Section: Previous Workmentioning

confidence: 99%

“…However, conventional (non-interactive) script and language identification are still in its early stage of research [8], and have remained unexplored until very recently [7].…”

Section: Previous Workmentioning

confidence: 99%

“…A series of n-gram language identification models were proposed in [7]. In this work, we applied the best performing models, the unigram model…”

Section: Probabilistic Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Multilinguality is captured by language identification models already discussed in [7]. The problem of OOV words is tackled by deploying character-based n-gram language models.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Character-Based Handwritten Text Recognition of Multilingual Documents

Agua

Serrano

Civera

et al. 2012

Advances in Speech and Language Technologies for Iberian Languages

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Interactive Transcription of Old Text Documents

Martínez-Santos¹

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Nowadays, there are huge collections of handwritten text documents in libraries all over the world. The high demand for these resources has led to the creation of digital libraries in order to facilitate the preservation and provide electronic access to these documents. However text transcription of these documents images are not always available to allow users to quickly search information, or computers to process the information, search patterns or draw out statistics. The problem is that manual transcription of these documents is an expensive task from both economical and time viewpoints. This thesis presents a novel approach for efficient Computer Assisted Transcription (CAT) of handwritten text documents using state-of-the-art Handwriting Text Recognition (HTR) systems.The objective of CAT approaches is to efficiently complete a transcription task through human-machine collaboration, as the effort required to generate a manual transcription is high, and automatically generated transcriptions from state-of-the-art systems still do not reach the accuracy required. This thesis is centered on a special application of CAT, that is, the transcription of old text document when the quantity of user effort available is limited, and thus, the entire document cannot be revised. In this approach, the objective is to generate the best possible transcription by means of the user effort available. This thesis provides a comprehensive view of the CAT process from feature extraction to user interaction.First, a statistical approach to generalise interactive transcription is proposed. As its direct application is unfeasible, some assumptions are made to apply it to two different tasks. First, on the interactive transcription of handwritten text documents, and next, on the interactive detection of the document layout.Next, the digitisation and annotation process of two real old text documents is described. This process was carried out because of the scarcity of similar resources and the need of annotated data to thoroughly test all the developed tools and techniques in this thesis. These two documents were carefully selected to represent the general difficulties that are encountered when dealing with HTR. Baseline results are presented on these two documents to settle down a benchmark with a standard HTR system. Finally, these annotated documents were made freely available to the community. It must be noted that, all the techniques and methods vii developed in this thesis have been assessed on these two real old text documents.Then, a CAT approach for HTR when user effort is limited is studied and extensively tested. The ultimate goal of applying CAT is achieved by putting together three processes. Given a recognised transcription from an HTR system. The first process consists in locating (possibly) incorrect words and employs the user effort available to supervise them (if necessary). As most words are not expected to be supervised due to the limited user effort available, only a few are selected to be revised. The s...

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Language Identification for Interactive Handwriting Transcription of Multilingual Documents

Cited by 2 publications

References 6 publications

Character-Based Handwritten Text Recognition of Multilingual Documents

Character-Based Handwritten Text Recognition of Multilingual Documents

Interactive Transcription of Old Text Documents

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