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# Symmetry-based text detection #
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1. Introduction.
The source code of symmetry-based text detection algorithm. We adopt approximate calculation technology and parallel technology to speed up the proposed algorithm.
For this reason, the probability output has a slightly difference but very close result on ICDAR dataset, compared to the CVPR'15 version.
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2. Installation.
a) This code is written for the Windows x64 and Visual Studio 2012, and you may needs OpenCV 2.4.10 and VL-feat.
b) You should check the config.txt for parameter setting.
The config.txt is consists of several lines:
line #1: Dataset Path
line #2: Mode flag(0 or 1), 0 is testing mode and 1 is training mode
line #3: The normalized height of input image
line #4: number of sliding window scales for each octave
line #5: the minimum sliding window scale, it would be 2^min_scale
line #6: the maximum sliding window scale, it would be 2^min_scale
line #7: Only for training stage. This parameter indicate an absolute distance.
Pixels whose distance greater than this distance are regard as negative samples.
line #8: Only for training stage. This parameter indicate an relative distance(relative to the height of ground truth, Eg: if the height of ground truth is 10 and the paprameter is 0.2, the distance is 2).
Pixels whose distance less than this distance are regard as positive samples
line #9: Only for training stage. The maximum negative samples in training stage.
line #10: Only for training stage. The maximum positive samples in training stage.
line #11: the bin number of lab channel
line #12: the bin number of gradient channel
line #13: the bin number of texture channel. In current version, this parameter is fixed by 58
line #14: the angle threshold for symmetry line linkage
line #15: the core number for parallel
line #16: the shrink step, it should be 2^n
and We support an example in the package. Note that the setting of config.txt(line #11 ~ line#13) should be keep consistent during training and testing.
c) Prepare the dataset and create folders.
The directory structure is described as bellow:
->Dataset\
->Annotation\
->train\
->Feature
->train\
->Images
->train\
->test\
->Model
->Result
And for annotation files, each of them should contains several lines, and each line should contain 4 numbers: x, y, width, height.
For more details, please refer to the sample in the package.
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3. Getting Started.
- Make sure to carefully follow the installation instructions above.
- Set config.txt.
- Run TextlineDetection.exe config.txt in command line.
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