文件名称:Connected-Component-based-text-region-extraction.
- 所属分类：
- matlab例程
- 资源属性：
- [Matlab] [源码]
- 上传时间：
- 2014-12-16
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- 41kb
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- 提 供 者：
- Lee K*****
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The basic steps of the connected-component text extraction algorithm are given below,
and diagrammed in Figure 10. The details are discussed in the following sections.
1. Convert the input image to YUV color space. The luminance(Y) value is used for
further processing. The output is a gray image.
2. Convert the gray image to an edge image.
3. Compute the horizontal and vertical projection profiles of candidate text regions
using a histogram with an appropriate threshold value.
4. Use geometric properties of text such as width to height ratio of characters to
eliminate possible non-text regions.
5. Binarize the edge image enhancing only the text regions against a plain black
background.
6. Create the Gap Image (as explained in the next section) using the gap-filling
process and use this as a reference to further eliminate non-text regions the
output.
-The basic steps of the connected-component text extraction algorithm are given below,
and diagrammed in Figure 10. The details are discussed in the following sections.
1. Convert the input image to YUV color space. The luminance(Y) value is used for
further processing. The output is a gray image.
2. Convert the gray image to an edge image.
3. Compute the horizontal and vertical projection profiles of candidate text regions
using a histogram with an appropriate threshold value.
4. Use geometric properties of text such as width to height ratio of characters to
eliminate possible non-text regions.
5. Binarize the edge image enhancing only the text regions against a plain black
background.
6. Create the Gap Image (as explained in the next section) using the gap-filling
process and use this as a reference to further eliminate non-text regions the
output.
and diagrammed in Figure 10. The details are discussed in the following sections.
1. Convert the input image to YUV color space. The luminance(Y) value is used for
further processing. The output is a gray image.
2. Convert the gray image to an edge image.
3. Compute the horizontal and vertical projection profiles of candidate text regions
using a histogram with an appropriate threshold value.
4. Use geometric properties of text such as width to height ratio of characters to
eliminate possible non-text regions.
5. Binarize the edge image enhancing only the text regions against a plain black
background.
6. Create the Gap Image (as explained in the next section) using the gap-filling
process and use this as a reference to further eliminate non-text regions the
output.
-The basic steps of the connected-component text extraction algorithm are given below,
and diagrammed in Figure 10. The details are discussed in the following sections.
1. Convert the input image to YUV color space. The luminance(Y) value is used for
further processing. The output is a gray image.
2. Convert the gray image to an edge image.
3. Compute the horizontal and vertical projection profiles of candidate text regions
using a histogram with an appropriate threshold value.
4. Use geometric properties of text such as width to height ratio of characters to
eliminate possible non-text regions.
5. Binarize the edge image enhancing only the text regions against a plain black
background.
6. Create the Gap Image (as explained in the next section) using the gap-filling
process and use this as a reference to further eliminate non-text regions the
output.
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下载文件列表
Algorithm for Connected Component based text region extraction.docx
cc.m