A method for extracting machine-readable labels for text boxes and symbols in piping and instrumentation drawings, P&IDs, in image-only formats, comprising: predicting, by an optical character recognition, OCR, algorithm of a P&ID data extraction application executing on a computing device, bounding boxes and text within text boxes in a P&ID in an image-only format, wherein the predicted text within text boxes are labels for the text boxes; detecting, by a first machine learning algorithm of the P&ID data extraction application that is based on a Faster Region-based Convolutional Neural Network, R-CNN, architecture, symbols in the P&ID, the detecting to return a predicted bounding box and predicted class of equipment for each symbol; decimating one or more of the predicted bounding boxes to avoid overlapping detections using a non-maximum suppression algorithm; inferring, by a second machine learning algorithm of the P&ID data extraction application that is based on a different deep neural network architecture adapted for properties classification, a number of properties for each detected symbol having a remaining predicted bounding box, wherein the number of properties depends on the predicted class of equipment and each property has a number of property values; storing, in a machine-readable format in a memory of the computing device for each text box, the bounding box and label for the text box, and, for each detected symbol having a remaining predicted bounding box, the predicted bounding box and label including the predicted class of equipment and inferred properties; and displaying the predicted bounding boxes and labels for the text boxes and the symbols in a user interface. predicting, by an optical character recognition, OCR, algorithm of a P&ID data extraction application executing on a computing device, bounding boxes and text within text boxes in a P&ID in an image-only format, wherein the predicted text within text boxes are labels for the text boxes; detecting, by a first machine learning algorithm of the P&ID data extraction application that is based on a Faster Region-based Convolutional Neural Network, R-CNN, architecture, symbols in the P&ID, the detecting to return a predicted bounding box and predicted class of equipment for each symbol; decimating one or more of the predicted bounding boxes to avoid overlapping detections using a non-maximum suppression algorithm; inferring, by a second machine learning algorithm of the P&ID data extraction application that is based on a different deep neural network architecture adapted for properties classification, a number of properties for each detected symbol having a remaining predicted bounding box, wherein the number of properties depends on the predicted class of equipment and each property has a number of property values; storing, in a machine-readable format in a memory of the computing device for each text box, the bounding box and label for the text box, and, for each detected symbol having a remaining predicted bounding box, the predicted bounding box and label including the predicted class of equipment and inferred properties; and displaying the predicted bounding boxes and labels for the text boxes and the symbols in a user interface.