TY  - RPRT
U1  - Forschungsbericht
A1  - Valdenegro Toro, Matias Alejandro
T1  - Fast Text Detection for Road Scenes
N2  - Extraction of text information from visual sources is an important component of many modern applications, for example, extracting the text from traffic signs on a road scene in an autonomous vehicle. For natural images or road scenes this is a unsolved problem. In this thesis the use of histogram of stroke widths (HSW) for character and noncharacter region classification is presented. Stroke widths are extracted using two methods. One is based on the Stroke Width Transform and another based on run lengths. The HSW is combined with two simple region features– aspect and occupancy ratios– and then a linear SVM is used as classifier. One advantage of our method over the state of the art is that it is script-independent and can also be used to verify detected text regions with the purpose of reducing false positives. Our experiments on generated datasets of Latin, CJK, Hiragana and Katakana characters show that the HSW is able to correctly classify at least 90% of the character regions, a similar figure is obtained for non-character regions. This performance is also obtained when training the HSW with one script and testing with a different one, and even when characters are rotated. On the English and Kannada portions of the Chars74K dataset we obtained over 95% correctly classified character regions. The use of raycasting for text line grouping is also proposed. By combining it with our HSW-based character classifier, a text detector based on Maximally Stable Extremal Regions (MSER) was implemented. The text detector was evaluated on our own dataset of road scenes from the German Autobahn, where 65% precision, 72% recall with a f-score of 69% was obtained. Using the HSW as a text verifier increases precision while slightly reducing recall. Our HSW feature allows the building of a script-independent and low parameter count classifier for character and non-character regions.
T3  - Technical Report / Hochschule Bonn-Rhein-Sieg University of Applied Sciences. Department of Computer Science - 05-2015 
KW  - text detection
KW  - text localization
KW  - road
KW  - scenes
KW  - optical character recognition
UN  - https://nbn-resolving.org/urn:nbn:de:hbz:1044-opus-15936
SN  - 1869-5272
SS  - 1869-5272
SN  - 978-3-96043-024-7
SB  - 978-3-96043-024-7
U6  - https://doi.org/10.18418/978-3-96043-024-7
DO  - https://doi.org/10.18418/978-3-96043-024-7
SP  - 124
S1  - 124
ER  -