![]() and use cv::dnn::TextDetectionModel_EAST with "EAST" models.use cv::dnn::TextDetectionModel_DB with "DB" models.In the current version, cv::dnn::TextDetectionModel supports these algorithms: cv::dnn::TextDetectionModel::detectTextRectangles() returns the results in std::vector (RBOX-like).cv::dnn::TextDetectionModel::detect() returns the results in std::vector> (4-points quadrangles).Other decoding methods may supported in the futureĬv::dnn::TextDetectionModel API provides these methods for text detection:.The input image should be a cropped text image or an image with roiRects.To further optimize for big vocabulary, a new option vocPruneSize is introduced to avoid iterate the whole vocbulary but only the number of vocPruneSize tokens with top probability.Ĭv::dnn::TextRecognitionModel::recognize() is the main function for text recognition.setDecodeOptsCTCPrefixBeamSearch could be used to control the beam size in search step.The algorithm is proposed at Hannun's paper."CTC-prefix-beam-search", the output of the text recognition model should be a probability matrix same with "CTC-greedy".and Dim is the length of vocabulary +1('Blank' of CTC is at the index=0 of Dim).B is the batch size (only support B=1 in inference). ![]() "CTC-greedy", the output of the text recognition model should be a probability matrix.For more information, please refer to the original paperīefore recognition, you should setVocabulary and setDecodeType. In the current version, cv::dnn::TextRecognitionModel only supports CNN+RNN+CTC based algorithms, and the greedy decoding method for CTC is provided. In this tutorial, we will introduce the APIs for TextRecognitionModel and TextDetectionModel in detail. Next Tutorial: DNN-based Face Detection And Recognition With this article at OpenGenus, you must have the complete idea of how to write Text on image in custom font in Python using PIL and OpenCV.Prev Tutorial: How to run custom OCR model Image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR) Once everything is done, save the image with text: image = omarray(image)įollowing is the complete Python code to write Text on image in custom font in Python using PIL and OpenCV: import cv2 You need to switch to the next line with a new dimension to accomodate longer sentences.Ĭonvert the image back to OpenCV format if you need to use OpenCV function for some functions: image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR) If the text cannot fit on a single line on the image, then part of the text that goes outside the image is not printed. Note that the text will be written on a single line irrespective of the length of the text. You shall give the point correctly to have the text on the correct location. (width, height) is the left top-most point of the text. Load the image in the form required by ImageDraw of PIL: image = np.load("image.jpeg")įont = uetype("localfont.ttf", font_size)ĭraw the text on the image: text = "opengenus"ĭraw.text((width, height), str(text), font=font) Import the relevant libraries: import cv2 ![]() The font file say cyrillic.ttf is kept locally to be used by your script. Usually, it is downloadable in OTF format so you need to convert it to TTF format. You need to convert the font file to TTF format. To install PIL in your system: pip install pillowĭownload the font file of the Font you want to use. Once the text is written over the image using PIL, the image can be transferred back to be used by OpenCV. The alternative is to use Python Imaging Library (PIL) which has the support to use custom fonts to write text over images. ![]() Unfortunately, it is not possible in OpenCV to use a font beyond the standard set. OpenCV provides a set of standard fonts that a developer can use to write text on an image. ![]() In this article, we have explained how to use custom font in Python, OpenCV to write text over an image. ![]()
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