I am working on a project where I need to draw alphabetic characters using draw(rect:) method. Until now I am able to draw these hand-drawn characters but I am unable to export these characters as strings. I tried to get the drawn alphabets using screenshots and OCR but all other algorithms just detect the test and resample the text to redefined shapes whereas, i need to convert the drawn character as it to the string or ".ttf".
If your goal is to have the user draw characters and turn their drawings into a font, you have your work cut out for you.
I suggest you have the user draw one character at a time. Since you know which one they are drawing, you know what character you're getting.
You'd record the series of gestures the user makes to draw the character, not the resulting pixels.
You'd then need to convert their raw gestures into a series of bezier curves. Then you'd need to encode those bezier curves into a glyph in the font you are building.
I've never figured out how to generate Bezier curves from points along the curve, since the middle control points for a Bezier curve don't lie along the curve. I've read that it's possible however.
As to encoding the curves into a glyph in TrueType font format, I got nuttin'. I suggest googling it.
Related
I have grayscale image containing noised lines. How to extract this lines?
I am trying with OpenCV's fitLine and least square methods in composition with sliding window, but it doesn't work due to high noise.
If you want only the straight lines, you can use the Hough Transform. You will get a lot potential lines, so you will have to filter them.
However, if you want more curvy lines, I would try the path opening created by Vincent Morard.
IEEE paper
The full list of publications
You can observe on the different images, that the "paths" can reconnect discontinuous curvy lines.
There is a similar paper here.
How to get an array of coordinates of a (drawn) line in image? Coordinates should be relative to image borders. Input: *.img . Output array of coordinates (with fixed step). Any 3rd party software to do this? For example there is high contrast difference - white background and color black line; or red and green etc.
Example:
Oh, you mean non-straight lines. You need to define a "line". Intuitively, you might mean a connected area of the image with a high aspect ratio between the length of its medial axis and the distance between medial axis and edges (ie relatively long and narrow, even if it winds around). Possible approach:
Threshold or select by color. Perhaps select by color based on a histogram of colors, or posterize as described here: Adobe Photoshop-style posterization and OpenCV, then call scipy.ndimage.measurements.label()
For each area above, skeletonize. Helpful tutorial: "Skeletonization using OpenCV-Python". However, you will likely need the distance to the edges as well, so use skimage.morphology.medial_axis(..., return_distance=True)
Do some kind of cleanup/filtering on the skeleton to remove short branches, etc. Thinking about your particular use, and assuming your lines don't loop around, you can just find the longest single path in the skeleton. This is where you can also decide if a shape is a "line" or not, based on how long the longest path in its skeleton is, relative to distance to the edges. Not sure how to best do that in opencv, but "Analyze Skeleton" in Fiji/ImageJ will let you filter by branch length.
What is left is the most elongated medial axis of the original "line" shape. You can resample that to some step that you prefer, or fit it with a spline, etc.
Due to the nature of what you want to do, it is hard to come up with a sample code that will work on a range of images. This is likely to require some careful tuning. I recommend using a small set of images (corpus), running any version of your algo on them and checking the results manually until it is pretty good, then trying it on a large corpus.
EDIT: Original answer, only works for straight lines:
You probably want to use the Hough transform (OpenCV tutorial).
Python sample code: Horizontal Line detection with OpenCV
EDIT: Related question with sample code to skeletonize: How can I get a full medial-axis line with its perpendicular lines crossing it?
Im trying to build some text using blocks, which I intend to customize later on. The attached image is a mockup of what i intend to do.
I was thinking of using WebGL, since I want to do it in 3D and I cant do any flash, but Im not sure how to contruct the structure of cubes from the letters. Can anyone give me a suggestion or a technique to map text to a series of points so that seen from far aside they draw that same text?
First, you need a font — a table of shapes for the characters — in a format you can read from your code. Do you already have one? If it's just a few letters, you could manually create polygons for each character.
Then, use a rasterization algorithm to convert the character shape into an array of present-or-absent points/cubes. If you have perfectly flat text, then use a 2D array; if your “customizations” will create depth effects then you will want a 3D array instead (“extruding” the shape by writing it identically into multiple planes of the array).
An alternative to the previous two steps, which is appropriate if your text does not vary at runtime, is to first create an image with your desired text on it, then use the pixels of the image as the abovementioned 2D array. In the browser, you can do this by using the 2D Canvas feature to draw an image onto a canvas and then reading the pixels out from it.
Then to produce a 3D shape from this voxel array, construct a polygon face for every place in the array where a “present” point meets an “absent” point. If you do this based on pairs of neighbors, you get a chunky pixel look (like Minecraft). If you want smooth slopes (like your example image), then you need a more complex technique; the traditional way to produce a smooth surface is marching cubes (but just doing marching cubes will round off all your corners).
I want to identify an object and draw a shape around it ...
I used previously the color identification but wasn't a good option since color change dramatically from place to place .. so I though why not identifying objects by features such as edges .. and I did that using this function in openCV
cvgoodfeaturesTotrack
it returns the (x,y)-coordinates of the points .. now I want to connect those points.. well not all of them but the one who are close to each other to draw a shape around the different objects. Any ideas ?
I don't think there is a free lunch in this case. You are trying to reconstruct a polygon if you only know the corner points of the polygon. There is no unique solution to this problem: you can draw all sorts of polygons through the corners. If you are certain the shape you are after is convex, then you can construct the convex span of the corner points, but the result will be horrible if you include any corners that were not part of the original object.
It seems to me that detecting corners is not the way to segment an object that is more or less delimited by lines. You probably want to try an edge detector instead, or a proper segmentation technique such as watershed.
Let's say I have some system that scans documents, where all documents use the same font and font size.
In these documents, there will always be the same looking letter "W". Let's say it is always 20 px large. How can I set up the hough transform to recognize this letter "W" at 20 px large in my documents?
A quick Google search yields the following information of interest:
Generalizing the Hough Transform to Detect Arbitrary Shapes
and it looks like a lecture using the above paper as its source.
Also, if it's an actual "W", would an OCR engine like Tesseract be better suited to your needs?
The Hough transform for lines finds best fit line equations. You would need to do additional processing to find just the line segments. If the character thickness is several pixels, then to effectively find lines you might want to reduce the thickness to one pixel. There are techniques to do that, but also various algorithmic traps.
Once you have your line segments, you would still have to write an algorithm to identify characters based on the relative position and angle of the line segments. It's harder than it first appears.
A normalized cross-correlation (template matching) could work if you're certain that the image will always be in a certain rotation, the characters will always be the same size, etc. But even for scans you'll see some rotation and some variation in contrast.
All that aside, it's likely cheaper in the long run to use a commercial OCR package or reasonably good open source project. OCR is hard to implement if you're not already familiar with image processing.