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