Extraction of embedded and/or line-touching character-like objects

Zhong, David X.

doi:10.1016/s0031-3203(01)00225-4

Cited by 8 publications

(4 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Li et al [9] proposed an OCR-based method by training prototypes of characters separated by an operator as training data using template matching approach. They assume some characters of text layer in the map are separable before completely text layer separation.…”

Section: Cao and Tanmentioning

confidence: 99%

Text Separation from Graphics by Analyzing Stroke Width Variety in Persian City Maps

Ghafari-Beranghar¹,

Kabir²,

Kangarloo³

2018

ijacsa

View full text Add to dashboard Cite

Text segmentation is a live research field with vast new areas to be explored. Separating text layer from graphics is a fundamental step to exploit text and graphics information. The language used in the map is a challenging issue in text layer separation problem. All current methods are proposed for non-Persian language maps. In Persian, text strings are composed of one or more subwords. Each subword is also composed of one to several letters connected together. Therefore, the components of the text strings in Persian are more diverse in terms of size and geometric form than in English. Thus, the overlapping of the Persian text and the lines usually produces a complex structure that the existing methods cannot handle with the necessary efficiency. For this purpose, the stroke width variety of the input map is calculated, and then the average line width of graphics is estimated by analyzing the content of stroke width. After finding the average width of graphical lines, we classify the complex structure into text and graphics in pixel level. We evaluate our method on some variety of full crossing text and graphics in Persian maps and show that some promising results in terms of precision and recall (above 80% and 90%, respectively) are obtained.

show abstract

Section: Cao and Tanmentioning

confidence: 99%

Text Separation from Graphics by Analyzing Stroke Width Variety in Persian City Maps

Ghafari-Beranghar¹,

Kabir²,

Kangarloo³

2018

ijacsa

View full text Add to dashboard Cite

show abstract

“…The touching characters are segmented by cutting the connected components in the pre-segmentation process. Zhong [8] has given a novel method of circle scanning to detect intersection regions and to extract embedded/line touching character objects. Jindal et al [9] have discussed an algorithm for segmentation of touching characters in upper zone of Gurmukhi script.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Isolated and Touching Characters in Offline Handwritten Gurmukhi Script Recognition

Kumar¹,

Jindal²,

Sharma³

2014

IJITCS

View full text Add to dashboard Cite

Abstract-Segmentation of a word into characters is one of the important challenges in optical character recognition. This is even more challenging when we segment characters in an offline handwritten document. Touching characters make this problem more complex. In this paper, we have applied water reservoir based technique for identification and segmentation of touching characters in handwritten Gurmukhi words. Touching characters are segmented based on reservoir base area points. We could achieve 93.51% accuracy for character segmentation with this method. If the characters are neither broken nor overlapping, then this technique shall produce even better results.

show abstract

“…Methods for recognition in maps are particularly challenging due to the complexity of map content (Cordella and Vento 2000;Watanabe 2000) and the presence of both single and composite map elements (Llados et al 2002). Composite elements consist of different graphic elements whose spatial distribution and patterns can define higher-level spatial objects such as paths (Gamba and Mecocci 1999), linework (Yamada et al 1993;Zhong 2002), or areas (Leyk et al 2006). The recognition of such complex objects is particularly problematic in maps of low quality, such as historical cartographic documents, which are often hand-drawn with Extracting Composite Cartographic Area Features in Low-Quality Maps…”

Section: Introductionmentioning

confidence: 99%