Language-Independent Text-Line Extraction Algorithm for Handwritten Documents

Text-line extraction in handwritten documents is an important step for document image understanding, and a number of algorithms have been proposed to address this problem. However, most of them exploit features of specific languages and work only for a given language. In order to overcome this limit...

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Vydáno v:IEEE signal processing letters Ročník 21; číslo 9; s. 1115 - 1119
Hlavní autoři: Ryu, Jewoong, Koo, Hyung Il, Cho, Nam Ik
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.09.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Connected component based algorithm
Cost function
Data mining
Estimates
Extraction
Feature extraction
handwritten documents
language-independent algorithm
Minimization
Partitioning algorithms
Partitions
Segmentation
Signal processing algorithms
State of the art
text-line extraction
text-line segmentation
ISSN:1070-9908, 1558-2361
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Shrnutí:Text-line extraction in handwritten documents is an important step for document image understanding, and a number of algorithms have been proposed to address this problem. However, most of them exploit features of specific languages and work only for a given language. In order to overcome this limitation, we develop a language-independent text-line extraction algorithm. Our method is based on connected components (CCs), however, unlike conventional methods, we analyze strokes and partition under-segmented CCs into normalized ones. Due to this normalization, the proposed method is able to estimate the states of CCs for a range of different languages and writing styles. From the estimated states, we build a cost function whose minimization yields text-lines. Experimental results show that the proposed method yields the state-of-the-art performance on Latin-based and Chinese script databases. Further, we submitted the proposed algorithm to the ICDAR 2013 handwriting segmentation competition and our method showed the best text-line extraction performance among 10 participant methods.
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ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2014.2325940