On recent versions of Windows, the recommended way to gather information from one application to another (if you don’t own the targeted application of course) is to use the UI Automation technology. Wikipedia is pretty good for more information on this: Microsoft UI Automation Basically, UI automation will use all necessary means to gather what … Read more
Since you’re on a Linux box, I would highly recommend Google’s open source project ocropus. It’s not PHP, but I think it will be your best option. Of course you can call it from within PHP via exec. Its mature and has a lot of options. From the project site: The OCRopus engine is based … Read more
This is how to do it … // // ViewController.swift // import UIKit import Vision import CoreML class ViewController: UIViewController { //HOLDS OUR INPUT var inputImage:CIImage? //RESULT FROM OVERALL RECOGNITION var recognizedWords:[String] = [String]() //RESULT FROM RECOGNITION var recognizedRegion:String = String() //OCR-REQUEST lazy var ocrRequest: VNCoreMLRequest = { do { //THIS MODEL IS TRAINED BY … Read more
There are a number of approaches you can take but the first strategy that pops into mind is to: Discovery/research: Identify the set of colors and fonts that you may need to identify. If your sample picture is representative of most British plates then your job is made easier. E.g. Simple, singular font and black … Read more
If anyone is looking into this, I’ve been trying different options and the following approach yields very good results. The following are the steps to get a working example: Add .NET Wrapper for tesseract to your project. It can be added via NuGet package Install-Package Tesseract(https://github.com/charlesw/tesseract). Go to the Downloads section of the official Tesseract … Read more
Use pytesseract.image_to_data() import pytesseract from pytesseract import Output import cv2 img = cv2.imread(‘image.jpg’) d = pytesseract.image_to_data(img, output_type=Output.DICT) n_boxes = len(d[‘level’]) for i in range(n_boxes): (x, y, w, h) = (d[‘left’][i], d[‘top’][i], d[‘width’][i], d[‘height’][i]) cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2) cv2.imshow(‘img’, img) cv2.waitKey(0) Among the data returned by pytesseract.image_to_data(): … Read more
tesseract-4.0.0a supports below psm. If you want to have single character recognition, set psm = 10. And if your text consists of numbers only, you can set tessedit_char_whitelist=0123456789. Page segmentation modes: 0 Orientation and script detection (OSD) only. 1 Automatic page segmentation with OSD. 2 Automatic page segmentation, but no OSD, or OCR. 3 Fully … Read more
Recognizing only numbers is actually answered on the tesseract FAQ page. See that page for more info, but if you have the version 3 package, the config files are already set up. You just specify on the commandline: tesseract image.tif outputbase nobatch digits As for the threshold value, I’m not sure which you mean. If … Read more
Like you I also faced many problems implementing OCR in Android, but after much Googling I found the solution, and it surely is the best example of OCR. Let me explain using step-by-step guidance. First, download the source code from https://github.com/rmtheis/tess-two. Import all three projects. After importing you will get an error. To solve the … Read more
EDIT: I wrote a Python script for this. As your objective is blurring (for privacy protection), you basically need a high recall detector as a first step. Here’s how to go about doing this. The included code hints use OpenCV with Python. Convert to Grayscale. Apply Gaussian Blur. img = cv2.imread(‘input.jpg’,1) img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) … Read more