I have all the characters of a font rendered in a PNG. I want to use the PNG for texture-mapped font rendering in OpenGL, but I need to extract the glyph information - character position and size, etc.
Are there any tools out there to assist in that process? Most tools I see generate the image file and glyph data together. I haven't found anything to help me extract from an existing image.
I use the gimp as my primary image editor, and I've considered writing a plugin to assist me in the process of identifying the bounding box for each character. I know python but haven't done any gimp plugins before, so that would be a chore. I'm hoping something already exists...
Generally, the way this works is you use a tool to generate the glyph images. That tool will also generate metric information for those glyphs (how big they are, etc). You shouldn't be analyzing the image to find the glyphs; you should have additional information alongside your glyphs that tell where they are, how big they should be, etc.
Consider the letter "i". Depending on the font, there will be some space to the left and right of it. Even if you have a tool that can identify the glyph "i", you would need to know how many pixels of space the font put to the left and right of the glyph. This is pretty much impossible to do accurately for all letters. Not without some very advanced computer vision algorithms. And since the tool that generated those glyphs already knew how much spacing they were supposed to get, you would be better off changing the tool to write the glyph info as well.
You can use PIL to help you automate the process.
Assuming there is at least one row/column of background colour separating lines/characters, you can use the Image.crop method to check each row (and then each column of the row) if it contains only the background colour; thus you get the borders of each character.
Ping if you need further assistance.
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Recently I wanted to make a script which could convert images into game levels (the game is Antiyoy online), the game can give out levels in a special text form which makes them easy to edit just by changing specific text.
I am rather begginer to Python and don't know much yet. I didn't start doing anything yet because I can't figure out which module should I use for the task of the image reading. I want for it to read the color of a chosen pixel on the image (and average out the color of multiple pixels for scaling if possible) and give out it in hex or other form in which I can convert it to the color closest to the ones selected by me.
Doing some research I'm pretty sure NumPy can do this but I have no experience with it and there are probably more specialized modules for this.
If what am I asking for is hard to understand I'm open for questions, thank you in advance.
I want to define an rectangular area on top of an image, that has got a specific width and a specific height and in which a text should be pasted. I want the text to stay inside of this text field. If the text is longer, its size should be decreased and at one point it should be converted into a multiline text. How can I define such a text field using Pillow in python3?
I am looking for a clean and minimalist solution.
Sadly, I have no idea how to do this, since I am very new to python. I also didn't find any helpful and fitting information on how to do this, in the Internet. Thanks in advance!
So, the idea here is that the given text, which happens to be Devanagari character such as संस्थानका कर्मचारी and I want to convert given text to image. Here is what I have attempted.
def draw_image(myString):
width=500
height=100
back_ground_color=(255,255,255)
font_size=10
font_color=(0,0,0)
unicode_text = myString
im = Image.new ( "RGB", (width,height), back_ground_color )
draw = ImageDraw.Draw (im)
unicode_font = ImageFont.truetype("arial.ttf", font_size)
draw.text ( (10,10), unicode_text, font=unicode_font, fill=font_color )
im.save("text.jpg")
if cv2.waitKey(0)==ord('q'):
cv2.destroyAllWindows()
But the font is not recognized, so the image consists of boxes, and other characters that are not understandable. So, which font should I use to get the correct image? Or is there any better approach to convert, the given text in character such as those, to image?
So I had a similar problem when I wanted to write text in Urdu onto images, firstly you need the correct font since writing purely with PIL or even openCV requires the appropriate Unicode characters, and even when you get the appropriate font the letters of one word are disjointed, and you don't get the correct results.
To resolve this you have to stray a bit from the traditional python-only approach since I was creating artificial datasets for an OCR, i needed to print large sets of such words onto a white background. I decided to use graphics software for this. Since some like photoshop even allows you to write scripts to automate processes.
The software I went for was GIMP, which allows you to quickly write and run extensions.scripts to automate the process. It allows you to write an extension in python, or more accurately a modified version of python, known as python-fu. Documentation was limited so it was difficult to get started, but with some persistence, I was able to write functions that would read text from a text file, and place them on white backgrounds and save to disk.
I was able to generate around 300k images from this in a matter of hours. I would suggest if you too are aiming for large amounts of text writing then you too rely on python-fu and GIMP.
For more info you may refer to the GIMP Python Documentation
i want to detect the font of text in an image so that i can do better OCR on it. searching for a solution i found this post. although it may seem that it is the same as my question, it does not exactly address my problem.
background
for OCR i am using tesseract, which uses trained data for recognizing text. training tesseract with lots of fonts reduces the accuracy which is natural and understandable. one solution is to build multiple trained data - one per few similar fonts - and then automatically use the appropriate data for each image. for this to work we need to be able to detect the font in image.
number 3 in this answer uses OCR to isolate image of characters along with their recognized character and then generates the same character's image with each font and compare them with the isolated image. in my case the user should provide a bounding box and the character associated with it. but because i want to OCR Arabic script(which is cursive and character shapes may vary depending on what other characters are adjacent to it) and because the bounding box may not be actually the minimal bounding box, i am not sure how i can do the comparing.
i believe Hausdorff distance is not applicable here. am i right?
shape context may be good(?) and there is a shapeContextDistanceExtractor class in opencv but i am not sure how i can use it in opencv-python
thank you
sorry for bad English
I'm trying to extract text from a scanned technical drawing. For confidentiality reasons, I cannot post the actual drawing, but it looks similar to this, but a lot busier with more text within shapes. The problem is quite complex due to issues with letters touching both each other and it's surrounding borders / symbols.
I found an interesting paper that does exactly this called "Detection of Text Regions From Digital Engineering Drawings" by Zhaoyang Lu. It's behind a paywall so you might not be able to access it, but essentially it tries to erase everything that's not text from the image through mainly two steps:
1) Erases linear components, including long and short isolated lines
2) Erases non-text strokes in terms of analysis of connected components of strokes
What kind of OpenCV functions would help in performing these operations? I would rather not write something from the ground up to do these, but I suspect I might have to.
I've tried using a template-based approach to try to isolate the text, but since the text location isn't completely normalized between drawings (even in the same project), it fails in detecting text past the first scanned figure.
I am working on a similar problem. Technical drawings are an issue because OCR software mostly tries to find text baselines and the drawing artifacts (lines etc) get in the way of that approach. In the drawing you specified there are not many characters touching each other. So I suggest to break the image into contiguous (black) pixels and then scan those individually. The height of the contiguous areas should give you also an indication if the contiguous area is text, or a piece of the drawing. To break the image into contiguous pixels, use a flood fill algorithm, and for the scanning Tesseract does a good job.
Obviously I've never attempted this specific task, however if the image really looks like the one you showed me I would start by removing all vertical and horizontal lines. This could be done pretty easily, just set a width threshold and for all pixels with intensity larger than some N value, and after that look the threshold amount of pixels perpendicular to the hypothethic line orientation. If it looks like a line erase it.
More elegant and perhaps better would be to do a hough transform for lines and circles and remove those elements that way.
Also you could maybe try some FFT based filtering, but I'm not so sure about that.
I've never used OpenCV but i would guess it can do the things i mentioned.