Stroke-Based Character Segmentation of Low-Quality Images on Ancient Chinese Tablet

Lu, Xiaofeng; Tang, Zhi; Liu, Yan; Gao, Liangcai; Wang, Ting; Wang, Zhipeng

doi:10.1109/icdar.2013.55

Cited by 3 publications

(3 citation statements)

References 26 publications

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“…The characters like O,D,B,Q,R,P and 6,8,9,0 are easily identified. [16] In analyzing the historical document, a novel feature extraction on recursive subdivision of the character image based on the center of masses at different levels of granularity is developed.…”

Section: Related Work In the Field Of Deciphering Epigraphymentioning

confidence: 99%

“…Perceptive approach in the automation of Sinhala [4], by segmenting the character using Adobe Photo shop tool and comparing the characters segmented with training data using correlation function. (d) Tamil Inscription [18] (e) Srilankan Inscription [4] (f) Thai Lamma Inscription [2] (g) Greek Inscription [16] (h) Srilankan Script Image [17] (i) Hoysala Inscription [12] Harappan civilization on the Indus river banks used a script which has survived on Seals, Pottery, Temple wall inscriptions, and copper. The script occurs mainly in the sign form having very minimal number of numeral and sign sets ranging up to 400.…”

Section: Related Work In the Field Of Deciphering Epigraphymentioning

confidence: 99%

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A Review on Automation of Ancient Epigraphical Images

Preethi¹,

H.R²

2016

IJDTA

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Inscriptions are the main source of historical study available throughout the world in constricted language. Epigraphy is the study of such inscriptions and the one who reads and understands (epigraphists or epigraphers) are in extinct condition, due to lack of knowledge transfer and interest. New inscriptions are found during excavation and finding expert epigraphers these days is a real challenge. Due to this, Researchers from the digital enhancement domain are actively involved in the decipherment of inscriptions all over the world. Automation Techniques uses Optical character recognition to convert the inscriptions into intelligible language. Currently works are witnessed in the literature survey on the global languages like Greek, Italian, Japanese, Russian, Latin, Iranian and Indian languages like Kannada, Tamil, Devanagari, Brahmi, Hoysala, and Pali. This paper reviews the techniques followed in automation of epigraphical scripts.

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Section: Related Work In the Field Of Deciphering Epigraphymentioning

confidence: 99%

Section: Related Work In the Field Of Deciphering Epigraphymentioning

confidence: 99%

A Review on Automation of Ancient Epigraphical Images

Preethi¹,

H.R²

2016

IJDTA

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“…In this manner, the task of distinguishing strokes from a complicated background is converted to the task of comparing the difference between skeletons of potential strokes and those of disturbing patches, both of which can be extracted from the resulting images of previous Stroke filters. Skeleton constraints such as length and width constraints can be introduced into the method to enhance stroke information [17].…”

Section: Related Workmentioning

confidence: 99%

Enhancement and Segmentation of Historical Records

Soumya¹,

G²

2015

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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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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Stroke-Based Character Segmentation of Low-Quality Images on Ancient Chinese Tablet

Cited by 3 publications

References 26 publications

A Review on Automation of Ancient Epigraphical Images

A Review on Automation of Ancient Epigraphical Images

Enhancement and Segmentation of Historical Records

Extracting Maya Glyphs from Degraded Ancient Documents via Image Segmentation

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