Question: I wanted to test a pretrained model downloaded from here to perform an ocr task. Link to download, its name is CRNN_VGG_BiLSTM_CTC.onnx. This model is extracted from here. The sample-image.png can be download from here (see the code bellow). When I do the forward of the neural network to predict (ocr) in the blob I get the following error:  error: OpenCV(4.4.0) /tmp/pip-req-build-xgme2194/opencv/modules/dnn/src/layers/convolution_layer.cpp:348: error: (-215:Assertion failed) ngroups > 0 && inpCn % ngroups == 0 && outCn % ngroups == 0 in function 'getMemoryShapes'  Feel free to read the code bellow. I tried many things, it's weird because this model does not require a predetermined input shape. If you know any way to read this model and do the forward it is also going to be helpful but I'd rather solve using OpenCV. import cv2 as cv # The model is downloaded from here https://drive.google.com/drive/folders/1cTbQ3nuZG-EKWak6emD_s8_hHXWz7lAr # model path modelRecognition = os.path.join(MODELS_PATH,'CRNN_VGG_BiLSTM_CTC.onnx') # read net recognizer = cv.dnn.readNetFromONNX(modelRecognition) # Download sample_image.png from https://i.ibb.co/fMmCB7J/sample-image.png (image host website) sample_image = cv.imread('sample-image.png') # Height , Width and number of channels of the image H, W, C = sample_image.shape # Create a 4D blob from cropped image blob = cv.dnn.blobFromImage(sample_image, size = (H, W)) recognizer.setInput(blob) # Here is where i get the errror that I mentioned before result = recognizer.forward()  Thank you so much in advance.

Answer: Your problem is actually that the input data you feed to your model doesn't match the shape of the data the model was trained on. I used this answer to inspect your onnx model and it appears that it expects an input of shape (1, 1, 32, 100). I modified your code to reshape the image to 1 x 32 x 100 pixels and the inference actually runs without error. EDIT I've added some code to interpret the result of the inference. We now display the image and the inferred OCR text. This doesn't seem to be working, but reading the tutorial on OpenCV, there should be two models:  one that detects where there is text in the image. This network accepts images of various sizes, it returns the locations of text within the image and then cropped parts of the image, of sizes 100x32 are passed to the second one that actually does the "reading" and given patches of image, returns the characters. For this, there a file alphabet_36.txt that is provided together with the pre-trained models.  It isn't clear to me though which network to use for text detection. Hope the edited code below helps you develop your application further. import cv2 as cv import os import numpy as np import matplotlib.pyplot as plt # The model is downloaded from here https://drive.google.com/drive/folders/1cTbQ3nuZG-EKWak6emD_s8_hHXWz7lAr # model path MODELS_PATH = './' modelRecognition = os.path.join(MODELS_PATH,'CRNN_VGG_BiLSTM_CTC.onnx') # read net recognizer = cv.dnn.readNetFromONNX(modelRecognition) # Download sample_image.png from https://i.ibb.co/fMmCB7J/sample-image.png (image host website) sample_image = cv.imread('sample-image.png', cv.IMREAD_GRAYSCALE) sample_image = cv.resize(sample_image, (100, 32)) sample_image = sample_image[:,::-1].transpose() # Height and Width of the image H,W = sample_image.shape # Create a 4D blob from image blob = cv.dnn.blobFromImage(sample_image, size=(H,W)) recognizer.setInput(blob) # network inference result = recognizer.forward() # load alphabet with open('alphabet_36.txt') as f: alphabet = f.readlines() alphabet = [f.strip() for f in alphabet] # interpret inference results res = [] for i in range(result.shape[0]): ind = np.argmax(result[i,0]) res.append(alphabet[ind]) ocrtxt = ''.join(res) # show image and detected OCR characters plt.imshow(sample_image) plt.title(ocrtxt) plt.show()  Hope it helps. Cheers