Recognition of Road Names in Maps
碩士 === 國立交通大學 === 資訊工程學系 === 84 === This thesis presents a system to identify road names from a map. A map consists of a large number of entities, such as geographic landmarks, cities, rivers, roads, grid lines, country borders, institution names, and cit...
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ndltd-TW-084NCTU03920282016-02-05T04:16:36Z http://ndltd.ncl.edu.tw/handle/91448732934427171752 Recognition of Road Names in Maps 地圖中道路名稱之辨認 Tsai, Chun-Ming 蔡俊明 碩士 國立交通大學 資訊工程學系 84 This thesis presents a system to identify road names from a map. A map consists of a large number of entities, such as geographic landmarks, cities, rivers, roads, grid lines, country borders, institution names, and city borders. Road names often distribute nonuniformly in a map. They may rotate, vary in size or font type, touch lines or touch each other seriously. These problems make character extraction and recognition of road names very difficult. This thesis presents some methods to solve these problems. We first propose a connected-component-based method to extract the index table and its characters. The operations include binarization, connected component analysis, rules checking, and understanding. Second, we propose an cascade OCR system to recognize the characters of the index table. The characters include Ming font, multi-size Chinese characters, English characters and numerals in a map. In this OCR system, statistical and structural features are used to recognize characters. This system consists of four phases: preprocessing, feature extraction, classification, and postprocessing. The preprocessing phase performs thresholding and masking. In the feature extraction phase, we compute four types of features, which are Walsh transformation, pixel distributions, crossing counts, and long horizontal-vertical strokes. In classification phase, preclassification and cascade fine matching are performed. Three kinds of features are used serially in the fine matching. In the final postprocessing phase, candidates outputted from the fine matching module are tuned according to the frequency rate trained from characters collected from a tour book. At last, the top two candidates with the highest scores are chosen as the recognition results. In the last part, a geometry-based algorithm is proposed to identify the road names of the index table. This algorithm includes road line extension, shortest distance coincidence, rules checking, and some constraint satisfaction. The testing maps of our experiments contain nine maps. All index tables and characters in the index tables can be extracted. The recognition rate of characters in index tables is 98.23%, and the identification rate of road names is 95.54%. These experimental results show that the proposed system is rather effective. Hsi-Jian Lee 李錫堅 1996 學位論文 ; thesis 70 zh-TW |
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碩士 === 國立交通大學 === 資訊工程學系 === 84 === This thesis presents a system to identify road names from a map.
A map consists of a large number of entities, such as geographic
landmarks, cities, rivers, roads, grid lines, country borders,
institution names, and city borders. Road names often
distribute nonuniformly in a map. They may rotate, vary in size
or font type, touch lines or touch each other seriously. These
problems make character extraction and recognition of road names
very difficult. This thesis presents some methods to solve
these problems. We first propose a connected-component-based
method to extract the index table and its characters. The
operations include binarization, connected component analysis,
rules checking, and understanding. Second, we propose an
cascade OCR system to recognize the characters of the index
table. The characters include Ming font, multi-size Chinese
characters, English characters and numerals in a map. In this
OCR system, statistical and structural features are used to
recognize characters. This system consists of four phases:
preprocessing, feature extraction, classification, and
postprocessing. The preprocessing phase performs thresholding
and masking. In the feature extraction phase, we compute four
types of features, which are Walsh transformation, pixel
distributions, crossing counts, and long horizontal-vertical
strokes. In classification phase, preclassification and cascade
fine matching are performed. Three kinds of features are used
serially in the fine matching. In the final postprocessing
phase, candidates outputted from the fine matching module are
tuned according to the frequency rate trained from characters
collected from a tour book. At last, the top two candidates
with the highest scores are chosen as the recognition results.
In the last part, a geometry-based algorithm is proposed to
identify the road names of the index table. This algorithm
includes road line extension, shortest distance coincidence,
rules checking, and some constraint satisfaction. The testing
maps of our experiments contain nine maps. All index tables and
characters in the index tables can be extracted. The
recognition rate of characters in index tables is 98.23%, and
the identification rate of road names is 95.54%. These
experimental results show that the proposed system is rather
effective.
|
author2 |
Hsi-Jian Lee |
author_facet |
Hsi-Jian Lee Tsai, Chun-Ming 蔡俊明 |
author |
Tsai, Chun-Ming 蔡俊明 |
spellingShingle |
Tsai, Chun-Ming 蔡俊明 Recognition of Road Names in Maps |
author_sort |
Tsai, Chun-Ming |
title |
Recognition of Road Names in Maps |
title_short |
Recognition of Road Names in Maps |
title_full |
Recognition of Road Names in Maps |
title_fullStr |
Recognition of Road Names in Maps |
title_full_unstemmed |
Recognition of Road Names in Maps |
title_sort |
recognition of road names in maps |
publishDate |
1996 |
url |
http://ndltd.ncl.edu.tw/handle/91448732934427171752 |
work_keys_str_mv |
AT tsaichunming recognitionofroadnamesinmaps AT càijùnmíng recognitionofroadnamesinmaps AT tsaichunming detúzhōngdàolùmíngchēngzhībiànrèn AT càijùnmíng detúzhōngdàolùmíngchēngzhībiànrèn |
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