2012 10th IAPR International Workshop on Document Analysis Systems 2012
DOI: 10.1109/das.2012.70
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Quality Evaluation of Facsimiles of Hebrew First Temple Period Inscriptions

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Cited by 15 publications
(16 citation statements)
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“…We therefore conclude that [11][12][13] demonstrate that the GT is inherently subjective, with large deviations between different human operators and creation techniques, influencing the performance of the algorithms "downstream". This problem was noted already in [14], where automatic systems were found to be more reliable than the human "ground truther".…”
Section: Quality Measurementioning
confidence: 97%
See 1 more Smart Citation
“…We therefore conclude that [11][12][13] demonstrate that the GT is inherently subjective, with large deviations between different human operators and creation techniques, influencing the performance of the algorithms "downstream". This problem was noted already in [14], where automatic systems were found to be more reliable than the human "ground truther".…”
Section: Quality Measurementioning
confidence: 97%
“…Article [12] deals with GTs of First Temple period Hebrew inscriptions, created by several experts. Their GTs were shown to be of markedly different quality.…”
Section: Quality Measurementioning
confidence: 99%
“…The evaluation is based on a manual GT creation, and on various GT-versusbinarization measures (e.g., F-measure, PSNR, Distance Reciprocal Distortion, Misclassification Penalty, etc.). Several recent papers [4][5][6] performed a detailed analysis of this approach, stressing its inherent weaknesses such as subjectivity and the inherent inconsistency within the GT creation process. Among the alternative solutions suggested, are skeleton-based GT variants (maintaining some degree of human intervention) [7][8], automatic GT creation (via another binarization procedure) [9], creation of synthetic document images out of existing GT (applicable if noise model exists) [10][11] and goal-directed approach, e.g.…”
Section: Introductionmentioning
confidence: 99%
“…Several features were adapted, referring to aspects such as the character's overall shape, the angles between strokes, the character's center of gravity, as well as its horizontal and vertical projections. The features in use were SIFT (28), Zernike (29), DCT, K d -tree (30), Image projections (31), L 1 , and CMI (32). Additionally, for each feature, a respective distance was defined.…”
Section: Methodsmentioning
confidence: 99%