![]() It is nevertheless possible to make live recognition of handwritten characters (ie: writing with a connected pen) Ask a new question Source codeĭCode retains ownership of the online "OCR - Character Recognition" source code. Then we open the created text file in append mode to append the obtained text and close the file.To our knowledge, there are no reliable handwriting recognition software, without a supervised learning process. Now crop the rectangular region and then pass it to the tesseract to extract the text from the image. There are 5 parameters in the cv2.rectangle(), the first parameter specifies the input image, followed by the x and y coordinates (starting coordinates of the rectangle), the ending coordinates of the rectangle which is (x+w, y+h), the boundary color for the rectangle in RGB value and the size of the boundary. Then draw a rectangle in the image using the function cv2.rectangle() with the help of obtained x and y coordinates and the width and height. Loop through each contour and take the x and y coordinates and the width and height using the function cv2.boundingRect(). This text file is opened to save the text from the output of the OCR. A text file is opened in write mode and flushed. All the above image processing techniques are applied so that the Contours can detect the boundary edges of the blocks of text of the image. ![]() Contours are typically used to find a white object from a black background. Each contour is a Numpy array of (x, y) coordinates of boundary points in the object. Contours is a python list of all the contours in the image. This function returns contours and hierarchy. There are three arguments in cv.findContours(): the source image, the contour retrieval mode and the contour approximation method. Dilation makes the groups of text to be detected more accurately since it dilates (expands) a text block.Ĭv2.findContours() is used to find contours in the dilated image. After choosing the correct kernel, dilation is applied to the image with cv2.dilate function. A bigger kernel would make group larger blocks of texts together. cv2.getStructuringElement takes an extra size of the kernel parameter. Here, we use the rectangular structural element (cv2.MORPH_RECT).
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