Character-Based Handwritten Text Recognition of Multilingual Documents

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

doi:10.1007/978-3-642-35292-8_20

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…On the other hand, OOVs and language modelling problems by sub-word based recognition (Agua et al, 2012).…”

Section: Discussionmentioning

confidence: 99%

“…However, user supervision at the character level may significantly reduce the effort needed to interactively transcribe a text document, specially in the presence of a large number of OOV words. For this reason, we are currently exploring this possibility to improve the performance of our interactive HTR system (Agua et al, 2012). Finally, an improved language model estimation could be obtained by successfully incorporating external resources, as explained in Section 4.3.7.…”

Section: Discussionmentioning

confidence: 99%

“…In HTR, for instance, España-Boquera et al (2011) employed user effort to train a NN to reduce the variability of input images. On the other hand, Agua et al (2012) showed the transcription of multilingual documents can be improved by manually specifying the language i which each line is written. However, the most common use of user interaction is to supervise the system output, in order to obtain a correct transcription.…”

Section: Interactive Htrmentioning

confidence: 99%

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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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“…On the other hand, OOVs and language modelling problems by sub-word based recognition (Agua et al, 2012).…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Interactive Htrmentioning

confidence: 99%

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Interactive Transcription of Old Text Documents

Martínez-Santos¹

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End-to-End Multilingual Text Recognition Based on Byte Modeling

Wu,

Zhao,

Yang

et al. 2023

Lecture Notes in Computer Science

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Interactive handwriting recognition with limited user effort

Serrano

Giménez

Civera

et al. 2013

IJDAR

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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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Character-Based Handwritten Text Recognition of Multilingual Documents

Cited by 5 publications

References 15 publications

Interactive Transcription of Old Text Documents

Interactive Transcription of Old Text Documents

End-to-End Multilingual Text Recognition Based on Byte Modeling

Interactive handwriting recognition with limited user effort

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