![]() # initialize our set of bounding box rectangles and corresponding # grab the number of rows and columns from the scores volume, then # python text_detection_video.py -east frozen_east_text_detection.pbĭef decode_predictions(scores, geometry): Installed Libraries from import VideoStreamįrom imutils.object_detection import non_max_suppression ![]() Last, run the project with the command “ py main.py” ![]() Next, import the source code you’ve download to your P圜harm IDE. Step 2: Import the project to your P圜harm IDE.Step 1: Download the given source code below.įirst, download the given source code below and unzip the source code.These are the steps on how to run Real-Time Text Detection OpenCV Python With Source Code In this Python OpenCV Project also includes a downloadable Python Project With Source Code for free, just find the downloadable source code below and click to start downloading. The EAST text detector requires that we are running OpenCV on our systems - if you do not already have OpenCV or better installed, please refer to my OpenCV install guides and follow the one for your respective operating system Project Information’s Project Name: OpenCV Python Text Detection With Source Code Language/s Used: Python (OpenCV) Python version (Recommended): 2.x or 3.x Database: None Type: Deep Learning Project Developer: IT SOURCECODE Updates: 0 OpenCV East Text Detection Python with Source Code – Project Information About The Project In this OpenCV Text Detection Python you will learn how to use OpenCV’s EAST detector to automatically detect text in both images and video streams.Īlso, you will learn how to use OpenCV to detect text in images using the EAST text detector. OpenCV Python is a deep learning model, based on a novel architecture and training pattern. What is the right (or a good) way to robustly filter for text like this? Is there a "standard" technique that can latch onto this kind of outline? I'm hoping to stick to image processing and basic heuristics, rather than more advanced discrimination like machine learning.The OpenCV Python Text Detection was developed using Python OpenCV, In this tutorial you will learn how to use OpenCV to detect text in real-time using web-camera. on the right here is a feature that looks quite "text-y" after thresholding.įiltering "small" contours is also challenging, as there are valid small strokes in Chinese, as well as punctuation like full stops and commas. ![]() I have tried to filter the image with a Laplace Pyramid filter (based on this example), but it's hard to find a filter that homes on the white text without often blowing noise in the background into pure white too. Some backgrounds are very noisy, so there can be a lot of spurious edges too (this is a bit contrived, as no filtering is done, and the Canny aperture is too big, but the double edges are visible): ![]() A naive first attempt that just thresholded the image is not sufficient, as it (predictably) produces artifacts in areas of white background (eg under the first and second characters in this example).Įdge detection is also challenging, as you get a decent "inner edge" for the white-black edge, but you might or might not get an outer edge for the black-background boundary (if the background is dark), or you might only get a partial or unclosed outline. What I'd like to achieve is something like this that can be fed to OCR: What I'd like to achieve is a "tidy" black-on-white image of the text.Įxample input, something like this, where the background is uncontrolled, and can also be as white as the text. The text in question is mostly in Chinese script (some numbers and Latin letters) of a known font (looks like SimHei), is white (ish) and has a black (ish) border, and occurs left-aligned at a constant location. I have some video frames I'd like to extract some text from (for making softsubs from hardsubbed video for learning). ![]()
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