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6993：License Plate Recognition
题目描述
Little Rabbit is a college student who is studying Communication Engineering. In this term, he has a course called Digital Image Processing. At the end of the course, Little Rabbit needs to complete a project called License Plate Recognition, in which Little Rabbit should program a system to recognize the characters of a license plate in an image.
A classic License Plate Recognition system has three modules:
After the License Plate Localization module and some preprocessing, Little Rabbit gets some binary images that represent the license plates. The binary images are all $100$ pixels in width and $30$ pixels in height, containing only black pixels and white pixels. The black pixels represent the background, and the white pixels represent the characters.
Little Rabbit's task is to segment the binary images so that the characters in the images can be separated. According to the standard, there are seven characters in a license plate, lining up from left to right. Specifically, Little Rabbit's task is to find the left boundary and the right boundary of each character.
Let's consider the images as matrices with $30$ rows and $100$ columns. Then number the columns $1$ to $100$ from left to right. We define the left boundary of a character as the index of the column where the leftmost pixel is located, and the right boundary as the index of the column where the rightmost pixel is located. For example, in the following picture, the left boundary of the character is $3$, and the right boundary is $7$.
Now given the binary images that Little Rabbit needs to segment, please output the left boundary and the right boundary of each character. In this problem, we use $\text{.}$ to represent a black pixel, and $\text{#}$ to represent a white pixel.
A classic License Plate Recognition system has three modules:
- License Plate Localization: to localize the license plate in the image.
- Character Segmentation: to segment the image so that the characters of the license plate can be separated.
- Character Recognition: to recognize the characters in the image and get the result string.
After the License Plate Localization module and some preprocessing, Little Rabbit gets some binary images that represent the license plates. The binary images are all $100$ pixels in width and $30$ pixels in height, containing only black pixels and white pixels. The black pixels represent the background, and the white pixels represent the characters.
Little Rabbit's task is to segment the binary images so that the characters in the images can be separated. According to the standard, there are seven characters in a license plate, lining up from left to right. Specifically, Little Rabbit's task is to find the left boundary and the right boundary of each character.
Let's consider the images as matrices with $30$ rows and $100$ columns. Then number the columns $1$ to $100$ from left to right. We define the left boundary of a character as the index of the column where the leftmost pixel is located, and the right boundary as the index of the column where the rightmost pixel is located. For example, in the following picture, the left boundary of the character is $3$, and the right boundary is $7$.
Now given the binary images that Little Rabbit needs to segment, please output the left boundary and the right boundary of each character. In this problem, we use $\text{.}$ to represent a black pixel, and $\text{#}$ to represent a white pixel.
输入解释
The first line contains an integer $T$ ($1 \le T \le 50$) --- the number of test cases.
Each test case represents a binary image, which contains $30$ lines of strings. Each line contains $100$ characters, either $\text{.}$ (a black pixel) or $\text{#}$ (a white pixel).
Here are all the characters that may appear in the image.
Chinese characters:
ASCII version: $\text{https://paste.ubuntu.com/p/B5pTWv7s6J/}$
(Backup: $\text{https://github.com/cjj490168650/plate/blob/main/chn.txt}$)
English and numeric characters:
ASCII version: $\text{https://paste.ubuntu.com/p/59bjvwY3Yr/}$
(Backup: $\text{https://github.com/cjj490168650/plate/blob/main/eng.txt}$)
It is guaranteed that:
Each test case represents a binary image, which contains $30$ lines of strings. Each line contains $100$ characters, either $\text{.}$ (a black pixel) or $\text{#}$ (a white pixel).
Here are all the characters that may appear in the image.
Chinese characters:
ASCII version: $\text{https://paste.ubuntu.com/p/B5pTWv7s6J/}$
(Backup: $\text{https://github.com/cjj490168650/plate/blob/main/chn.txt}$)
English and numeric characters:
ASCII version: $\text{https://paste.ubuntu.com/p/59bjvwY3Yr/}$
(Backup: $\text{https://github.com/cjj490168650/plate/blob/main/eng.txt}$)
It is guaranteed that:
- The characters in the image follow the standard of license plates. There are seven characters in the image, lining up from left to right. The first character is a Chinese character. The second character is an English character. The last five characters are English or numeric characters.
- All characters in the image are identical to the characters given above (ASCII version), including the size and the shape.
- There are no redundant white pixels in the image.
- There is spacing between characters.
- The characters won't touch or get out of the image boundaries.
输出解释
For the $x$-th test case, output $\text{Case #x:}$ in the first line.
Then in the next seven lines, the $i$-th line contains two integers separated by a space character, indicating the left boundary and the right boundary of the $i$-th character.
Then in the next seven lines, the $i$-th line contains two integers separated by a space character, indicating the left boundary and the right boundary of the $i$-th character.
输入样例
1 .................................................................................................... .................................................................................................... .................................................................................................... .................................................................................................... .................................................................................................... .................................................................................................... .................................................................................................... ..........................##...........##..........#####......########..........##......########.... .......#.......#..........###..........##.........#######.....########.........###......########.... .......#.......#..........###..........##........##....##..........###.........###......##.......... .......#.......#.........####..........##...............##........###.........####......##.......... ....###############......####..........##..............##........###..........####......##.......... .......#.......#.........##.#..........##..............##.......###...........####......##.......... .......#.......#.........##.##.........##..............##.......#####........#####......#######..... .......#.......#.........##.##.........##.............##........######.......##.##......########.... .......#.......#........###.##.........##............###............##......###.##............###... .......#########........##..##.........##...........###.............##......##..##.............##... .......#.......#........##...#.........##...........##..............##......##..##.............##... .......#.......#........##...##........##..........###..............##.....##...##.............##... .......#.......#........#######........##.........###.........##....##.....#########...........##... .......#.......#.......########........##.........##..........##....##.....#########....##.....##... .......#########.......##....##........##........###..........###...##..........##.......##...###... .......#.......#.......##....##........##........########......######...........##.......#######.... .......................##.....##.......##........########.......####............##........#####..... .................................................................................................... .................................................................................................... .................................................................................................... .................................................................................................... .................................................................................................... ....................................................................................................
输出样例
Case #1: 5 19 24 32 40 41 50 58 63 70 76 84 89 97
最后修改于 2021-10-23T19:10:56+00:00 由爬虫自动更新
共提交 4 次
通过率 50.0%
| 时间上限 | 内存上限 |
| 4000/2000MS(Java/Others) | 262144/262144K(Java/Others) |
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