Document image binarization using background estimation and stroke edges

Lu, Shijian; Su, Bolan; Tan, Chew Lim

doi:10.1007/s10032-010-0130-8

Cited by 203 publications

(124 citation statements)

References 16 publications

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“…They achieve their results partially by using thresholds, but most also perform modeling of the ink and background classes to enable more accurate individual pixel classification. Lu et al [14] model the document background via an iterative polynomial smoothing, and then choose local thresholds based on detected text stroke edges. This method won DIBCO 2009, and a revised version tied as a winner of H-DIBCO 2010 (handwritten documents only).…”

Section: Prior Workmentioning

confidence: 99%

Document binarization with automatic parameter tuning

Howe

2012

IJDAR

160

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Document analysis systems often begin with binarization as a first processing stage. Although numerous techniques for binarization have been proposed, the results produced can vary in quality and often prove sensitive to the settings of one or more control parameters. This paper examines a promising approach to binarization based upon simple principles, and shows that its success depends most significantly upon the values of two key parameters. It further describes an automatic technique for setting these parameters in a manner that tunes them to the individual image, yielding a final binarization algorithm that can cut total error by one-third with respect to the baseline version. The results of this method advance the state of the art on recent benchmarks.

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Section: Prior Workmentioning

confidence: 99%

Document binarization with automatic parameter tuning

Howe

2012

IJDAR

160

150

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“…In [Lu et al 2010], a document background model is first estimated through an interactive polynomial smoothing procedure. This method has achieved the top performance in the Document Image Binarization Contest (DIBCO) 2009 [Gatos et al 2009].…”

Section: Image Segmentationmentioning

confidence: 99%

Extracting Maya Glyphs from Degraded Ancient Documents via Image Segmentation

Odobez

Gática-Pérez

2017

J. Comput. Cult. Herit.

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We present a system for automatically extracting hieroglyph strokes from images of degraded ancient Maya codices. Our system adopts a region-based image segmentation framework. Multi-resolution super-pixels are first extracted to represent each image. A SVM classifier is used to label each super-pixel region with a probability to belong to foreground glyph strokes. Pixel-wise probability maps from multiple super-pixel resolution scales are then aggregated to cope with various stroke widths and background noise. A fully connected Conditional Random Field (CRF) model is then applied to improve the labeling consistency. Segmentation results show that our system preserves delicate local details of the historic Maya glyphs with various stroke widths, and also reduces background noise. As an application, we conduct retrieval experiments using the extracted binary images. Experimental results show that our automatically extracted glyph strokes achieve comparable retrieval results to those obtained using glyphs manually segmented by epigraphers in our team. Two Maya hieroglyph image datasets are shared for academic research, which can be used as image segmentation and shape analysis benchmarks, and also to study the ancient Maya writing system.

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“…In addition, handwritten text documents are degraded due to a certain amount of variation in terms of the stroke width, stroke brightness, stroke connection, and document background as illustrated in Figure 1 [4] held under the framework of the International Conference on Frontiers in Handwritten Recognition show recent efforts on this issue. We participated in the DIBCO 2009 and our background estimation method [5] performs the best among entries of 43 algorithms submitted from 35 international research groups. We also participated in the H-DIBCO 2010 and our local maximum-minimum method [6] was one of the top two winners among 17 submitted algorithms.…”

Section: Introductionmentioning

confidence: 99%