The lifecycle of a digital historical document

Antonacopoulos, Apostolos; Karatzas, Dìmosthenis; Krawczyk, Henryk; Wiszniewski, Bogdan

doi:10.1145/1030397.1030427

Cited by 14 publications

(7 citation statements)

References 3 publications

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“…Other digitization projects [26,27] illustrate a similar lifecycle. Figure 1 illustrates the automated processing phase as well as a manual correction phase.…”

Section: ) Deploymentmentioning

confidence: 92%

Document digitization lifecycle for complex magazine collection

Yacoub¹,

Burns²,

Faraboschi³

et al. 2005

Proceedings of the 2005 ACM Symposium on Document Engineering

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The conversion of large collections of documents from paper to digital formats that are suitable for electronic archival is a complex multi-phase process. The creation of good quality images from paper documents is just one phase. To extract relevant information that they contain, with an accuracy that fits the purpose of target applications, an automated document analysis system and a manual verification/review process are needed. The automated system needs to perform a variety of analysis and recognition tasks in order to reach an accuracy level that minimizes the manual correction effort downstream.This paper describes the complete process and the associated technologies, tools, and systems needed for the conversion of a large collection of complex documents and deployment for online web access to its information rich content. We used this process to recapture 80 years of Time magazines. The historical collection is scanned, automatically processed by advanced document analysis components to extract articles, manually verified for accuracy, and converted in a form suitable for web access. We discuss the major phases of the conversion lifecycle and the technology developed and tools used for each phase. We also discuss results in terms of recognition accuracy.

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“…Other digitization projects [26,27] illustrate a similar lifecycle. Figure 1 illustrates the automated processing phase as well as a manual correction phase.…”

Section: ) Deploymentmentioning

confidence: 92%

Document digitization lifecycle for complex magazine collection

Yacoub¹,

Burns²,

Faraboschi³

et al. 2005

Proceedings of the 2005 ACM Symposium on Document Engineering

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“…Documents within the same semantic class can be processed throughout the rest of the cycle in a specific way, "tuned" individually for each class defined. This has been made possible by introducing two concepts: a document template, and phase tuning (the latter is achieved through rigorous evaluation of quality at different levels using a Quality Evaluation of electronic Documents tool (QED), described elsewhere [2]). …”

Section: Digital Historical Document Life-cyclementioning

confidence: 99%

“…Results from applying different thresholding methods at different segmentation levels are shown in Figure 3 (again, for brevity, results on lines and words are omitted). Note that in the "transport list" class, the best results are obtained by applying the GPP [2] method at the region level, whereas in the "catalogue card" class, the application of Niblack [5] thresholding at the individual character level yields the best results.…”

Section: Text Segmentation and Enhancementmentioning

confidence: 99%

Semantics-based content extraction in typewritten historical documents

Antonacopoulos¹,

Karatzas²

2005

Eighth International Conference on Document Analysis and Recognition (ICDAR'05)

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“…LITERATURE REVIEW An ample study of research outcome in several related areas are surveyed. A significant need to devise a suitable document architecture that supports these activities and to use it to represent the original paper document in electronic form [1].It is highly essential and unavoidable while dealing with Indian languages for which there has been little digital access. Tamil is one of the 16 major national languages spoken by the South Indian.…”

Section: Introductionmentioning

confidence: 99%

An extended method for recognition of broken typewritten characters special reference to tamil script

Abubacker

Gandhi²

2011

2011 IEEE Conference on Open Systems

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Preparing clean and clear images for the recognition engines is often taken for granted as a trivial task that requires little attention. Most of the existing OCRs have been designed in such a way that which correctly identify fine printed documents in all scripts. The performance of standard machine printed OCR system works fails, if it is tested on documents with distorted characters. This paper presents an approach to overcome the difficulties presented in such distorted type written documents especially with broken characters. As a first step, isolation of character is forwarded using character position location and character localization and enclosing it in a matrix which will be analyzing and repairing in the later part of our study. An attempt is incorporated using shape and line tracing method for recognition of distorted broken characters and then it is fine tuned by lexical knowledge.

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The lifecycle of a digital historical document

Cited by 14 publications

References 3 publications

Document digitization lifecycle for complex magazine collection

Document digitization lifecycle for complex magazine collection

Semantics-based content extraction in typewritten historical documents

An extended method for recognition of broken typewritten characters special reference to tamil script

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