Annotating Historical Archives of Images

Keogh, Eamonn; Shelton, Christian R.; Ye, Lexiang; Wang, Xiaoyue

doi:10.4018/jdls.2010040104

Cited by 4 publications

(4 citation statements)

References 16 publications

(26 reference statements)

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“…and use the mean of those values as the threshold for the rare initial letters from the same text (here, 'K', 'A', 'M', etc.). Our second solution is inspired by [31], which also faced the problem of scarce training data in historical manuscripts. As shown in Figure 11.right, we can take singleton or rare initial letters and produce slightly distorted versions of them.…”

Section: Learning the Threshold Distancementioning

confidence: 99%

Image Mining of Historical Manuscripts to Establish Provenance

Rakthanmanon

Campana

et al. 2012

Proceedings of the 2012 SIAM International Conference on Data Mining

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he recent digitization of more than twenty million books has been led by initiatives from countries wishing to preserve their cultural heritage and by commercial endeavors, such as the Google Print Library Project. Within a few years a significant fraction of the world's books will be online. For millions of intact books and tens of millions of loose pages, the provenance of the manuscripts may be in doubt or completely unknown, thus denying historians an understanding of the context of the content. In some cases it may be possible for human experts to regain the provenance by examining linguistic, cultural and/or stylistic clues. However, such experts are rare and this investigation is clearly a time-consuming process. One technique used by experts to establish provenance is the examination of the ornate initial letters appearing in the questioned manuscript. By comparing the initial letters in the manuscript to annotated initial letters whose origin is known, the provenance can be determined. In this work we show for the first time that we can reproduce this ability with a computer algorithm. We leverage off a recently introduced technique to measure texture similarity and show that it can recognize initial letters with an accuracy that rivals or exceeds human performance. A brute force implementation of this measure would require several years to process a single large book; however, we introduce a novel lower bound that allows us to process the books in minutes.

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Section: Learning the Threshold Distancementioning

confidence: 99%

Image Mining of Historical Manuscripts to Establish Provenance

Rakthanmanon

Campana

et al. 2012

Proceedings of the 2012 SIAM International Conference on Data Mining

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“…Since k-beam search ensures the selection of all the nodes that satisfy the condition specified in (26), it, therefore, guarantees no false negatives.…”

Section: Propositionmentioning

confidence: 98%

“…These approaches achieve rotation invariance by compromising on the accuracy. Approaches using 1D time series representation of shapes has also been proposed [2,3,[24][25][26]. Some of these approaches [25] achieve rotation invariance by selecting very few starting point (alignment to major axis) to obtain 1D time series representation of 2D shape.…”

Section: Background and Related Workmentioning

confidence: 99%

Incremental indexing and retrieval mechanism for scalable and robust shape matching

Khalid

2011

Multimedia Systems

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Techniques for efficient and effective contentbased image matching are becoming increasingly important with the widespread increase in digital image capturing systems. Shape of an object, represented by its contour, is one of the most important visual feature that is thought to be used by humans to determine the similarity of objects. The selected feature and its distance measure must be robust to different distortions such as noise, articulation, scale and rotation. Existing approaches provides invariance to these distortions at the cost of either the accuracy due to poor discrimination ability or the efficiency. In this paper, we present an effective representation of shape, using its boundary information, that is robust to arbitrary distortions and affine transformation. The contour representation of shape is converted into time series and is modeled using orthogonal basis function representations. Shape matching is then carried out in the chosen coefficient feature space resulting in efficient matching. The efficiency of shape matching is further improved by indexing the shape descriptors using hierarchical indexing structure. A novel distributed beam search based technique is proposed that operates on the indexing structure and ensures no false dismissal for a given k-NN query. Experimental evaluation demonstrates that the proposed shape representation and matching mechanism is robust, efficient and scalable to very large shape datasets.

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“…Open Annotation Data Model [49,50,57] allows users to associate additional information with an existing Web resource or a specific part of it. It is called open because the model utilizes Linked Data [51] principles to connect open Web resources together.…”

Section: Open Annotationmentioning

confidence: 99%

Improving Accessibility of Archived Raster Dictionaries of Complex Script Languages

Alam

Mehmood

Nelson

2015

Proceedings of the 15th ACM/IEEE-CS Joint Conference on Digital Libraries

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We propose an approach to index raster images of dictionary pages which in turn would require very little manual effort to enable direct access to the appropriate pages of the dictionary for lookup. Accessibility is further improved by feedback and crowdsourcing that enables highlighting of the specific location on the page where the lookup word is found, annotation, digitization, and fielded searching. This approach is equally applicable on simple scripts as well as complex writing systems. Using our proposed approach, we have built a Web application called "Dictionary Explorer" which supports word indexes in various languages and every language can have multiple dictionaries associated with it. Word lookup gives direct access to appropriate pages of all the dictionaries of that language simultaneously. The application has exploration features like searching, pagination, and navigating the word index through a tree-like interface. The application also supports feedback, annotation, and digitization features. Apart from the scanned images, "Dictionary Explorer" aggregates results from various sources and user contributions in Unicode. We have evaluated the time required for indexing dictionaries of different sizes and complexities in the Urdu language and examined various tradeoffs in our implementation. Using our approach, a single person can make a dictionary of 1,000 pages searchable in less than an hour.

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Annotating Historical Archives of Images

Cited by 4 publications

References 16 publications

Image Mining of Historical Manuscripts to Establish Provenance

Image Mining of Historical Manuscripts to Establish Provenance

Incremental indexing and retrieval mechanism for scalable and robust shape matching

Improving Accessibility of Archived Raster Dictionaries of Complex Script Languages

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