消除图像中的背景噪音，使OCR的文字更清晰 [英] Remove background noise from image to make text more clear for OCR

问题描述

我编写了一个应用程序，该应用程序根据图像中的文本区域对图像进行分段，并在我认为合适的情况下提取这些区域.我正在尝试清洁图像，以便OCR(Tesseract)提供准确的结果.我以以下图片为例:

I've written an application that segments an image based on the text regions within it, and extracts those regions as I see fit. What I'm attempting to do is clean the image so OCR (Tesseract) gives an accurate result. I have the following image as an example:

通过tesseract运行此命令会产生广泛不准确的结果.但是，使用以下方法清理图像(使用photoshop)以获取图像:

Running this through tesseract gives a widely inaccurate result. However cleaning up the image (using photoshop) to get the image as follows:

准确给出我期望的结果.第一张图像已经通过以下方法运行，以将其清除到这一点:

Gives exactly the result I would expect. The first image is already being run through the following method to clean it to that point:

 public Mat cleanImage (Mat srcImage) {
    Core.normalize(srcImage, srcImage, 0, 255, Core.NORM_MINMAX);
    Imgproc.threshold(srcImage, srcImage, 0, 255, Imgproc.THRESH_OTSU);
    Imgproc.erode(srcImage, srcImage, new Mat());
    Imgproc.dilate(srcImage, srcImage, new Mat(), new Point(0, 0), 9);
    return srcImage;
}

我还能做些什么来清洁第一张图像，使其与第二张图像相似?

What more can I do to clean the first image so it resembles the second image?

这是通过cleanImage函数运行之前的原始图像.

This is the original image before it's run through the cleanImage function.

推荐答案

我的回答基于以下假设.在您的情况下，它们可能都不成立.

My answer is based on following assumptions. It's possible that none of them holds in your case.

• 您可能会为分割区域中的边界框高度强加一个阈值.然后，您应该可以过滤掉其他组件.
• 您知道数字的平均笔划宽度.使用此信息可以最大程度地减少数字连接到其他区域的可能性.您可以为此使用距离变换和形态学运算.

这是我提取数字的过程:

This is my procedure for extracting the digits:

• 将Otsu阈值应用于图像
• 进行距离变换

• 使用笔划宽度(= 8)约束将距离变换后的图像阈值化

• Apply Otsu threshold to the image
• Take the distance transform

• Threshold the distance transformed image using the stroke-width ( = 8) constraint

应用形态学运算以断开连接

Apply morphological operation to disconnect

过滤边界框高度并猜测数字的位置

Filter bounding box heights and make a guess where the digits are

笔划宽度= 8 笔划宽度= 10

stroke-width = 8 stroke-width = 10

编辑

• 使用找到的手指轮廓的凸包准备遮罩

• Prepare a mask using the convexhull of the found digit contours

使用遮罩将数字区域复制到干净的图像

Copy digits region to a clean image using the mask

笔划宽度= 8

笔划宽度= 10

我的Tesseract知识有点生锈.我记得您可以获得角色的置信度.如果您仍然碰巧将嘈杂的区域检测为字符边界框，则可以使用此信息滤除噪声.

My Tesseract knowledge is a bit rusty. As I remember you can get a confidence level for the characters. You may be able to filter out noise using this information if you still happen to detect noisy regions as character bounding boxes.

C ++代码

Mat im = imread("aRh8C.png", 0);
// apply Otsu threshold
Mat bw;
threshold(im, bw, 0, 255, CV_THRESH_BINARY_INV | CV_THRESH_OTSU);
// take the distance transform
Mat dist;
distanceTransform(bw, dist, CV_DIST_L2, CV_DIST_MASK_PRECISE);
Mat dibw;
// threshold the distance transformed image
double SWTHRESH = 8;    // stroke width threshold
threshold(dist, dibw, SWTHRESH/2, 255, CV_THRESH_BINARY);
Mat kernel = getStructuringElement(MORPH_RECT, Size(3, 3));
// perform opening, in case digits are still connected
Mat morph;
morphologyEx(dibw, morph, CV_MOP_OPEN, kernel);
dibw.convertTo(dibw, CV_8U);
// find contours and filter
Mat cont;
morph.convertTo(cont, CV_8U);

Mat binary;
cvtColor(dibw, binary, CV_GRAY2BGR);

const double HTHRESH = im.rows * .5;    // height threshold
vector<vector<Point>> contours;
vector<Vec4i> hierarchy;
vector<Point> digits; // points corresponding to digit contours

findContours(cont, contours, hierarchy, CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));
for(int idx = 0; idx >= 0; idx = hierarchy[idx][0])
{
    Rect rect = boundingRect(contours[idx]);
    if (rect.height > HTHRESH)
    {
        // append the points of this contour to digit points
        digits.insert(digits.end(), contours[idx].begin(), contours[idx].end());

        rectangle(binary, 
            Point(rect.x, rect.y), Point(rect.x + rect.width - 1, rect.y + rect.height - 1),
            Scalar(0, 0, 255), 1);
    }
}

// take the convexhull of the digit contours
vector<Point> digitsHull;
convexHull(digits, digitsHull);
// prepare a mask
vector<vector<Point>> digitsRegion;
digitsRegion.push_back(digitsHull);
Mat digitsMask = Mat::zeros(im.rows, im.cols, CV_8U);
drawContours(digitsMask, digitsRegion, 0, Scalar(255, 255, 255), -1);
// expand the mask to include any information we lost in earlier morphological opening
morphologyEx(digitsMask, digitsMask, CV_MOP_DILATE, kernel);
// copy the region to get a cleaned image
Mat cleaned = Mat::zeros(im.rows, im.cols, CV_8U);
dibw.copyTo(cleaned, digitsMask);

编辑

Java代码

Mat im = Highgui.imread("aRh8C.png", 0);
// apply Otsu threshold
Mat bw = new Mat(im.size(), CvType.CV_8U);
Imgproc.threshold(im, bw, 0, 255, Imgproc.THRESH_BINARY_INV | Imgproc.THRESH_OTSU);
// take the distance transform
Mat dist = new Mat(im.size(), CvType.CV_32F);
Imgproc.distanceTransform(bw, dist, Imgproc.CV_DIST_L2, Imgproc.CV_DIST_MASK_PRECISE);
// threshold the distance transform
Mat dibw32f = new Mat(im.size(), CvType.CV_32F);
final double SWTHRESH = 8.0;    // stroke width threshold
Imgproc.threshold(dist, dibw32f, SWTHRESH/2.0, 255, Imgproc.THRESH_BINARY);
Mat dibw8u = new Mat(im.size(), CvType.CV_8U);
dibw32f.convertTo(dibw8u, CvType.CV_8U);

Mat kernel = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(3, 3));
// open to remove connections to stray elements
Mat cont = new Mat(im.size(), CvType.CV_8U);
Imgproc.morphologyEx(dibw8u, cont, Imgproc.MORPH_OPEN, kernel);
// find contours and filter based on bounding-box height
final double HTHRESH = im.rows() * 0.5; // bounding-box height threshold
List<MatOfPoint> contours = new ArrayList<MatOfPoint>();
List<Point> digits = new ArrayList<Point>();    // contours of the possible digits
Imgproc.findContours(cont, contours, new Mat(), Imgproc.RETR_CCOMP, Imgproc.CHAIN_APPROX_SIMPLE);
for (int i = 0; i < contours.size(); i++)
{
    if (Imgproc.boundingRect(contours.get(i)).height > HTHRESH)
    {
        // this contour passed the bounding-box height threshold. add it to digits
        digits.addAll(contours.get(i).toList());
    }   
}
// find the convexhull of the digit contours
MatOfInt digitsHullIdx = new MatOfInt();
MatOfPoint hullPoints = new MatOfPoint();
hullPoints.fromList(digits);
Imgproc.convexHull(hullPoints, digitsHullIdx);
// convert hull index to hull points
List<Point> digitsHullPointsList = new ArrayList<Point>();
List<Point> points = hullPoints.toList();
for (Integer i: digitsHullIdx.toList())
{
    digitsHullPointsList.add(points.get(i));
}
MatOfPoint digitsHullPoints = new MatOfPoint();
digitsHullPoints.fromList(digitsHullPointsList);
// create the mask for digits
List<MatOfPoint> digitRegions = new ArrayList<MatOfPoint>();
digitRegions.add(digitsHullPoints);
Mat digitsMask = Mat.zeros(im.size(), CvType.CV_8U);
Imgproc.drawContours(digitsMask, digitRegions, 0, new Scalar(255, 255, 255), -1);
// dilate the mask to capture any info we lost in earlier opening
Imgproc.morphologyEx(digitsMask, digitsMask, Imgproc.MORPH_DILATE, kernel);
// cleaned image ready for OCR
Mat cleaned = Mat.zeros(im.size(), CvType.CV_8U);
dibw8u.copyTo(cleaned, digitsMask);
// feed cleaned to Tesseract

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