OpenCV images in Python are just NumPy arrays, so I cropped the images to 4:3, or 960x720 in this case, by using array slicing:. @param objects Vector of rectangles where each rectangle contains the detected object , the rectangles may be partially outside the original image. imgmsg_to_cv. Next we will do the same for English alphabets, but there is a slight change in data and feature set. You can find my code on GitHub. But for those scanned pdf, it is actually the image in essence. *Note: Put everything in a folder both the Python files, HaarCasCade XML file and face_data. We need an advanced python programmer, familiar with OO design patterns in python and preferably kivy to help accelerate this project The app will use a video feed from opencv, which we will provide the code for, you will then create a basic UI and interface with a number of REST APIs for sending and receiving data. Combine images into a video with Python 3 and OpenCv 3. Work with Git and GitHub directly from Atom with the GitHub package. 3d reconstruction from multiple images github, extract the text from the image using python. Image Analysis is a very common field in the area of Computer Vision. scikit-image is an image processing library that implements algorithms and utilities for use in research, education and industry applications. Please try the new VTKExamples website. Raw pixel data is hard to use for machine learning, and for comparing images in general. Download the image into the code directory; then read the image with OpenCV and show it: image = cv2. Deploy a Python application to an OpenShift cluster on IBM Cloud using Docker images in a local repository, a GitHub repo with sources and a Dockerfile, or a GitHub repo with sources using OpenShift Source-to-Image. In this introductory tutorial, you'll learn how to simply segment an object from an image based on color in Python using OpenCV. imshow() to display the image in a separate window. Coding Face Recognition using Python and OpenCV We are going to divide the Face Recognition process in this tutorial into three steps: Prepare Training Data: Read training images for each person/subject along with their labels, detect faces from each image and assign each detected face an integer label of the person it belongs. Tesseract was developed as a proprietary software by Hewlett Packard Labs. It is capable of (1) running at near real-time at 13 FPS on 720p images. Raw pixel data is hard to use for machine learning, and for comparing images in general. 9 Caffe is a deep learning framework popular in Linux with Python or Matlab interface. and tsherlock too !! (For respective usage and citations , take a look at the references) Now the real part. Examples of JES specific functions are:. Publisher's page. It is free software and provides a rich API in C, C++, Java and Python. You should see the OpenCV window open with your video stream and the words "OpenCV Image" printed across the image in yellow text; something like this: The test vision node is subscribing to the raw camera image on the /camera/raw topic. Skeletonization using OpenCV-Python , skeletonization algorithm, skeletonization code, skeletonization in image processing, skeletonization opencv. Extracting text from an image can be done with image processing. OpenCV is the most popular and advanced code library for Computer Vision related applications today, spanning from many very basic tasks (capture and pre-processing of image data) to high-level algorithms (feature extraction, motion tracking, machine learning). words, text lines, paragraphs, ). How is this done on an image? We will look at how an image is stored on a disc and how we can manipulate an image using this underlying data? Importing an Image. this code is not working with my 1024x1024 block of document image. Inputs, outputs and windowing. Along with Leptonica image processing it can recognize a wide variety of image formats and extract text. The good news is that you no longer have to. To read the subtitles out of my images, Google search led me to Tesseract. So, in this tutorial we performed the task of face recognition using OpenCV in less than 40 lines of python codes. Also, to test, I will be using images from Mare’s Computer Vision blog. OpenCV is a free open source library used in real-time image processing. IT was designed for computational efficiency and with a strong focus on real-time applications, video and image processing. I am looking for an approach / algorithm for using OCR (like Tesseract) to extract only bold text from an image. i have tried Below code, But i'm not getting expected result. Real-time Face Recognition: an End-to-end Project: On my last tutorial exploring OpenCV, we learned AUTOMATIC VISION OBJECT TRACKING. After the ER filtering is done on each input channel, character candidates must be grouped in high-level text blocks (i. In this article, we will learn how to read handwritten text from images using MS Azure. How to Detect Objects Using OpenCV & a Negative Image Set. Applied dilation to thicken lines in i. The Python code. In most situations, on the binary image we will need to open and close the image to remove small noise pixels and fill small holes. Also the image is directly taken from the camera. A few weeks ago I showed you how to perform text detection using OpenCV's EAST deep learning model. I am trying to detect text regions in the cropped image using contours. It would be appreciated if there are any Python VTK experts who could convert any of the c++ examples to Python!. In a fast, simple, yet extensible way. In this tutorial, we are going to build them ourselves. Automatic text detection and character recognition is just an example. Document recognition with Python, OpenCV and Tesseract Alexander Chebykin Recently I’ve conducted my own little experiment with the document recognition technology: I’ve successfully went from an image to the recognized editable text. Tutorial about how to convert image to text using Python+ OpenCv + OCR. 6 using OpenCV 3. This article explains how to use the SoftwareBitmap class, which is used by many different UWP APIs to represent images, with the Open Source Computer Vision Library (OpenCV), an open source, native code library that provides a wide variety of image processing algorithms. Working with OpenCV is fun and once you learn the basics you will find it pretty easy. Next we will do the same for English alphabets, but there is a slight change in data and feature set. That is the problem). Is it possible to set single threshold which removes noise from all images by doing some pre processing ?. Example source code of extract HOG feature from images, save descriptor values to xml file, using opencv (using HOGDescriptor ) This example source code is to extract HOG feature from images. In this tutorial, we demonstrate how to perform Hough Line and Circle detection using Emgu CV, as well as using the Contour class to detect Triangles and Rectangles in the image. Implemented in Python using OpenCV. Vipul Sharma's Blog. Select Sections From Images of Newspaper clippings, receipts etc Using Opencv and Python They are often not aligned properly. To be useful a face identification tool should be able to deal with images of any dimension containing several items : people, streets, cars, … As the VGG-Face model has been optimized on centered faces we will add a pre-processing step that extract faces from an images. Here is an alternative approach that I used to detect the text blocks: 1. The model is a highly accurate depth learning text detector, which can be used to detect text in natural scene images. We are going to compare the performance of different methods of image processing using three Python libraries (scipy, opencv and scikit-image). It mainly focuses on image processing, video capture and analysis including features like face detection and object detection. Convert Image Frames to Video File using OpenCV in Python This post will be helpful in learning OpenCV using Python programming. Here, we use Dlib for face detection and OpenCV for image transformation and cropping to produce aligned 96x96 RGB face images. Already have an account?. Simple Digit Recognition OCR in OpenCV-Python. This article explains how to use the SoftwareBitmap class, which is used by many different UWP APIs to represent images, with the Open Source Computer Vision Library (OpenCV), an open source, native code library that provides a wide variety of image processing algorithms. I have used the following wrapper for convenient feature extraction in TensorFlow. Extracting Contours with OpenCV. Using dominant color extraction we can assign appropriate colors for use in our plot automatically. We can write a program which allows us to select our desire portion in an image and extract that selected portion as well. And save descriptors to XML file. For this article, I've taken a picture from my Instagram account. The windows should appear on the desktop like in the above image. To begin your digital image processing application, Ubidots has created the following People Counting System tutorial using OpenCV and Python to analyze the number of people in a given area. Portable or not, the choice is yours! WinPython is a portable application, so the user should not expect any integration into Windows explorer during installation. Before we jump into the process of face detection, let us learn some basics about working with OpenCV. You can create instances of this class in several ways; either by loading images from files, processing other images, or creating images from scratch. Extract frames from pre-recored video with Python and OpenCV - video_to_frames. Then you can run the code below. v1 model was trained with aligned face images, therefore, the face images from the custom dataset must be aligned too. Processes an image to extract the text portions. imshow() to display the image in a separate window. Example source code of extract HOG feature from images, save descriptor values to xml file, using opencv (using HOGDescriptor ) This example source code is to extract HOG feature from images. XORing should do the rest of the job. Please read the first part of the tutorial here and then come back. worthwhile to take a glimpse of it. After getting the text, we had to write Python scripts OpenCV was the obvious choice to do image processing. If you open it, you will see 20000 lines which may, on first sight, look like garbage. Stackless Python - An enhanced version of the Python programming language. The Python code. The following program demonstrates. First thing we do is install pyzbar using pip. OpenCV’s SimpleBlobDetector will be the primary function that we will be using. The opencv_text module implements two different grouping algorithms: the Exhaustive Search algorithm proposed in for grouping horizontally aligned text, and the method proposed by Lluis. We need an advanced python programmer, familiar with OO design patterns in python and preferably kivy to help accelerate this project The app will use a video feed from opencv, which we will provide the code for, you will then create a basic UI and interface with a number of REST APIs for sending and receiving data. I am working on a project where I want to input PDF files, extract text from them and then add the text to the database. From the results of these quick tests it can be implied that: The OpenCV CUDA modules are being called from python. @param objects Vector of rectangles where each rectangle contains the detected object , the rectangles may be partially outside the original image. I tried using Tesseract on some of my images and its accuracy seems decent. sourceforge. The OpenCV library has an OCRTesseract class which gives more information other than text such as the location of text on the image and confidence score which can be useful. To read and display image using OpenCV Python, you could use cv2. worthwhile to take a glimpse of it. Along with Leptonica image processing it can recognize a wide variety of image formats and extract text. OpenCV provides us with a convenient method, cv2. You can operate the HSV (Hue, Saturation, Value) sliders to isolate the colour you want to detect in the image. (2) After using OpenCV to detect text areas in images, we extract each text ROI and input it into Tesseract to build a complete OpenCV OCR process!. Using OpenCV in your ROS code. CNN feature extraction in TensorFlow is now made easier using the tensorflow/models repository on Github. opencv documentation: Canny Algorithm - C++. ) implemented in python or C++? I would like to extract various image features for phone screenshot images recognition. GitHub for Atom. You can use it to extract metadata, rotate pages, split or merge PDFs and more. So far the results are promising but far from robust and lack high accuracy. Python Image Tutorial. Unlike other PDF-related tools, it focuses entirely on getting and analyzing text data. But data cleaning is done on datasets , tables , text etc. Extract frames from pre-recored video with Python and OpenCV - video_to_frames. Expand your applications beyond just people counting with the added resources of Ubidots IoT Development Platform. sourceforge. Extract text from PDF with Java PDF Read Write Extract Text: Reader/Writer/Extract Text Library/Component/API - Create, Modify, Read, Write PDF files and Extract text images from PDF files using Java - with FREE trial, source code can be obtained through licensing. Deploy a Python application to an OpenShift cluster on IBM Cloud using Docker images in a local repository, a GitHub repo with sources and a Dockerfile, or a GitHub repo with sources using OpenShift Source-to-Image. We will have to account for this when displaying the RGB text string. Table of Contents Random Forest Regression Using Python Sklearn From Scratch Recognise text and digit from the image with Python, OpenCV and Tesseract OCR Real-Time Object Detection Using YOLO Model Deep Learning Object Detection Model Using TensorFlow on Mac OS Sierra Anaconda Spyder Installation on Mac & Windows Install XGBoost on Mac OS. Edge Detection with High Pass Filter using openCV and NumPy. imread("image. Blending features from the second image on top of the first. by Chris Lovett, Byron Changuion, Ofer Dekel, and Kern Handa. Text mining is the application of natural language processing techniques and analytical methods to text data in order to derive relevant information. As part of Opencv 3. Using this model we were able to detect and localize the bounding box coordinates of text contained in an image. THRESH_BINARY along with the values to the remaining parameters. In the previous tutorial, we have used Background Subtraction, Motion Detection and Thresholding to segment our hand region from a live video sequence. After getting the text, we had to write Python scripts OpenCV was the obvious choice to do image processing. After my professor bought a Robotic Arm, I decided to do Hand Gesture Recognition. You can find my code on GitHub. bpython - A fancy interface to the Python interpreter. OpenCV computer vision. The operations to perform using OpenCV are such as Segmentation and contours, Hierarchy and retrieval mode, Approximating contours and finding their convex hull, Conex Hull, Matching Contour, Identifying Shapes (circle, rectangle, triangle, square, star), Line detection, Blob detection, Filtering. But for those scanned pdf, it is actually the image in essence. It was just an experiment. Extracting Contours with OpenCV. By using GitHub’s @mention system in your pull request message, you can ask for feedback from specific people or teams, whether they’re down the hall or 10 time zones away. Otherwise it will continue to extract frames from video infinitely. A digital image in its simplest form is just a matrix of pixel intensity values. Unfortunately OpenCV doesn’t come with prebuilt mingw/TDM (64 bit) binaries for windows. to transform an angled image (non-top-down clicked image) and display it as […]. ocr-text-extraction. One of my favorite is PyPDF2. Let’s improve on the emotion recognition from a previous article about FisherFace Classifiers. As mentioned in the first post, it’s quite easy to move from detecting faces in images to detecting them in video via a webcam - which is exactly what we will detail in this post. Also, the aspect ratio of the original image could be preserved in the resized image. Python & Matlab and Mathematica Projects for $750 - $1500. The operations to perform using OpenCV are such as Segmentation and contours, Hierarchy and retrieval mode, Approximating contours and finding their convex hull, Conex Hull, Matching Contour, Identifying Shapes (circle, rectangle, triangle, square, star), Line detection, Blob detection, Filtering. As an OpenCV novice, I searched Google to help me get started with the Python OpenCV code. Building blocks. Environment setup Download the source of OpenCV 3. Extract frames from pre-recored video with Python and OpenCV - video_to_frames. So we shall write a program in python using the module pytesseract that will extract text from any image. A digital image in its simplest form is just a matrix of pixel intensity values. The Hough Line Transform is a transform used to detect straight lines. I have used the following wrapper for convenient feature extraction in TensorFlow. I am selecting each number individually, performing image transformations (thresholding, image contrasting, averaging then contrasting) where none seem to provide a reliable and robust output. In this introductory tutorial, you'll learn how to simply segment an object from an image based on color in Python using OpenCV. The images we use for training our model must be of the same size. Make sure pip is linked to Python 3. Image Classification in Python with Visual Bag of Words (VBoW) Part 1. Related course: Machine Learning A-Z™: Hands-On Python & R In Data Science; OCR with tesseract. You can find my code on GitHub. So I decided to work on a project using this Neural Network from OpenCV and extract faces from images. If you have an image of background alone, like image of the room without visitors, image of the road without vehicles etc, it is an easy job. To find out more, including how to control cookies, see here: Cookie Policy %d bloggers like this:. Today's I'll be explaining how to extract text from images using the Java Tesseract API from net. Load and display an image with OpenCV. Work with Git and GitHub directly from Atom with the GitHub package. OpenCV is a free open source library used in real-time image processing. Next we will do the same for English alphabets, but there is a slight change in data and feature set. OpenCV provides us with a convenient method, cv2. A digital image in its simplest form is just a matrix of pixel intensity values. My goal is to extract the nutrient information of a food product. In a fast, simple, yet extensible way. Optical Character Recognition(OCR) is the process of electronically extracting text from images or any documents like PDF and reusing it in a variety of ways … Continue Reading. However, it is relatively straightforward to undistort with OpenCV. You can look at the video of our project here. Extract HOG features from these training samples. Beyond image recognition and object detection in images and videos, ImageAI supports advanced video analysis with interval callbacks and functions to train image recognition models on custom datasets. How to do Tesseract ocr for differrent language using Python | Extract text from image How to do Text to speech using python and OpenCV of How to read text from an image using python and. After following the steps and executing the Python code below, the output should be as follows, showing a video in which persons are tagged once recognized: Neural networks trained for object recognition allow one to identify persons in pictures. You can use it to extract metadata, rotate pages, split or merge PDFs and more. image_to_string(file,. Automatic number plate recognition (ANPR; see also other names below) is a mass surveillance method that uses optical character recognition on images to read the license plates on vehicles. In my class I have to create an application using two classifiers to decide whether an object in an image is an example of phylum porifera (seasponge) or some other object. From the results of these quick tests it can be implied that: The OpenCV CUDA modules are being called from python. IT was designed for computational efficiency and with a strong focus on real-time applications, video and image processing. Simple and effective coin segmentation using Python and OpenCV Posted on 22/06/2014 by Christian S. Today's I'll be explaining how to extract text from images using the Java Tesseract API from net. There are two annotation features that support optical character recognition (OCR): TEXT_DETECTION detects and extracts text from any image. Here, we will inspect a python script (named tk-img2video) which will combine images into a video. Applied threshold (simple binary threshold, with a handpicked value of 150 as the threshold value) 3. How to Use Camelot. Is it possible to set single threshold which removes noise from all images by doing some pre processing ?. data in opencv/samples/cpp/ folder. CNN feature extraction in TensorFlow is now made easier using the tensorflow/models repository on Github. I use OpenCV which is the most well supported open source computer vision library that exists today! Using it in Python is just fantastic as Python allows us to focus on the problem at hand without being bogged down by complex code. OpenCV for Android. Can you check my uploaded image. A text editor is at the core of a developer’s toolbox, but it doesn't usually work alone. To read and display image using OpenCV Python, you could use cv2. Hi All, I'm new bee for python openCV, can you help me to extract text from small image. OpenCV is a free open source library used in real-time image processing. So far the results are promising but far from robust and lack high accuracy. To read the subtitles out of my images, Google search led me to Tesseract. Here's the video Real time face recognition in Python using Opencv. I started using Python and OpenCV after learning some basics from Adrian's pyimagesearch website. Expand your applications beyond just people counting with the added resources of Ubidots IoT Development Platform. Optionally provides also the Rects for individual text elements found (e. By using the AlignDlib utility from the OpenFace project this is straightforward:. Please read the first part of the tutorial here and then come back. scikit-image is an image processing library that implements algorithms and utilities for use in research, education and industry applications. Extracting Images Missing Alt Text with DeepCrawl Head over to the Pythia GitHub page and click on the. With the SimpleBlobDetector, you can distinguish blobs in your image based on different parameters such as color, size, and shape. Features Lightweight and simple to use user interface;. Implemented in Python using OpenCV. If you don't have OpenCV installed on your machine, start by following Adrian Rosebrock's excellent tutorial on installing OpenCV 3. Automatic text detection and character recognition is just an example. The overhead from using the CPU and/or CUDA python interface instead of directly calling from C++ is small. In all these cases, first you need to extract the person or vehicles alone. This Opencv C++ tutorial is about extracting text from an image using Tesseract OCR libraries. If you have an image of background alone, like image of the room without visitors, image of the road without vehicles etc, it is an easy job. OpenCV’s SimpleBlobDetector will be the primary function that we will be using. To recognize the face in a frame, first you need to detect whether the face is. We do this by using the awesome sklearn machine learning library for Python. Tesseract is a tool that recognizes the text in images using Optical Character Recognition (OCR). In order to know more about Face Recognition click here. Originally developed by Intel, it was later supported by Willow Garage then Itseez. I clone the opencv repository from Github to get the. However, the WinPython Control Panel allows to "register" your distribution to Windows (see screenshot below). In this article we will learn how to extract basic information about a PDF using PyPDF2 … Continue reading Extracting PDF Metadata and Text with Python →. I have been working on an OCR project for business cards using opencv in python. PDFMiner is a tool for extracting information from PDF documents. Object Recognition In Any Background Using OpenCV Python In my previous posts we learnt how to use classifiers to do Face Detection and how to create a dataset to train a and use it for Face Recognition, in this post we are will looking at how to do Object Recognition to recognize an object in an image ( for example a book), using SIFT/SURF. Today's I'll be explaining how to extract text from images using the Java Tesseract API from net. 6 based quadcopter) in our town (Porto Alegre, Brasil), I decided to implement a tracking for objects using OpenCV and Python and check how the results would be using simple and fast methods like Meanshift. I tried using Tesseract on some of my images and its accuracy seems decent. You can look at the video of our project here. py Sign up for free to join this conversation on GitHub. The image above is the output from another image from which I am trying to extract all the numbers. I am looking for an approach / algorithm for using OCR (like Tesseract) to extract only bold text from an image. Image analysis intro using python & opencv. worthwhile to take a glimpse of it. This tutorial is designed to help you install OpenCV 3. My goal is to extract the nutrient information of a food product. Similar question asked that looks helpful: How to extract text from image using openCV or OCR tesseract It says: The fastest way (maybe not the ideal) is to implement the following steps: - Use OpenCV to detect the paper sheet or the text area; - Perform any processing necessary to deskew the image (if necessary); - Save the image to disk as TIFF;. let's overwrite the img variable with an RGB image, using the cv2. Following is the syntax of this method. Importantly, the pip install methods below also work for the OpenCV GUI such as imshow etc. OpenCV’s EAST text detector is a deep learning model, based on a novel architecture and training pattern. Skeletonization using OpenCV-Python , skeletonization algorithm, skeletonization code, skeletonization in image processing, skeletonization opencv. To have this, first you need to install Tesseract-OCR on your PC. -compose src). Converted the image to grayscale 2. Checkout these websites:. I started first experimenting with Tesseract and its wrapper, pytesseract in Python and used OpenCV for image processing and it worked perfectly right from the start!. It includes a PDF converter that can transform PDF files into other text formats (such as HTML). So, open you CMD/Terminal and paste this: pip install pyzbar. In the pre-processing step, we only did Gaussian blurring. Images can be easily scaled up and down using. The use of the minMaxLoc function deserves an explanation. I've added my own notes about the installation process , in case you run into some issues in compiling OpenCV 3. Please, add termination condition in case of video file. Extract Video Frames with Python and OpenCV. It is free software and provides a rich API in C, C++, Java and Python. In this article we will learn how to extract basic information about a PDF using PyPDF2 … Continue reading Extracting PDF Metadata and Text with Python →. Using OpenCV takes a mp4 video and produces a number of images. Jupyter Notebook (IPython) - A rich toolkit to help you make the most out of using Python. ) that you can use (though no source code available). Examples of JES specific functions are:. Learning objectives. I have used the following wrapper for convenient feature extraction in TensorFlow. Python Numpy Tutorial. Opencv C++ Code with Example for Feature Extraction and Detection using SURF Detector This OpenCV C++ Tutorial is about feature detection using SURF Detector. In this post: Python extract text from image Python OCR(Optical Character Recognition) for PDF Python extract text from multiple images in folder How to improve the OCR results Python's binding pytesseract for tesserct-ocr is extracting text from image or PDF with great success: str = pytesseract. Python-tesseract is a wrapper for Google's Tesseract-OCR Engine. OpenCV is a library of cross platform programming functions aimed at real time Computer Vision. The most popular library that is used for Computer Vision is the openCV library. In this tutorial, we shall the syntax of cv2. Extracting a ROI (Region of Interest) using OpenCV and Python is not so hard as it could may sound. There is a common saying, "A picture is worth a thousand words". In this tutorial you will learn how to extract text and numbers from a scanned image and convert a PDF document to PNG image using Python libraries such as wand, pytesseract, cv2, and PIL. 88/10 (yes, negative). x and TensorFlow 2. This article is ideal for anybody looking to use OpenCV in Raspberry Pi projects. I am working on a project where I want to input PDF files, extract text from them and then add the text to the database. If you open it, you will see 20000 lines which may, on first sight, look like garbage. Extracting tables from a PDF using Camelot is very simple. Then in function crop_rect(), we calculate a rotation matrix and rotate the original image around the rectangle center to straighten the rotated rectangle. Automatic license plate recognition (ALPR) has complex characteristics due to diverse effects such as of light and speed. I’ve added my own notes about the installation process , in case you run into some issues in compiling OpenCV 3. 0 and Python 2. pdftotext extracts text from a PDF while preserving the layout, using spaces. A text editor is at the core of a developer’s toolbox, but it doesn't usually work alone. Beyond image recognition and object detection in images and videos, ImageAI supports advanced video analysis with interval callbacks and functions to train image recognition models on custom datasets. Convert Image Frames to Video File using OpenCV in Python This post will be helpful in learning OpenCV using Python programming. The version I used was developed for Python called OpenCV-Python. 0] In this post we’re going to learn how to create an image classifier application with a proper GUI that allows the users to choose a camera or a video file as the input and classify …. How to do Tesseract ocr for differrent language using Python | Extract text from image How to do Text to speech using python and OpenCV of How to read text from an image using python and. In today’s post, we will learn how to recognize text in images using an open source tool called Tesseract and OpenCV. Processes an image to extract the text portions. Interactive Interpreter. Python Image Processing With OpenCV There are many image processing packages for Python. Welcome to a tutorial series, covering OpenCV, which is an image and video processing library with bindings in C++, C, Python, and Java. Importing an image in python is easy. We will have to account for this when displaying the RGB text string. To feed image into the network, we have to convert the image to a blob. Import GitHub Project I am working on visual studio 2010 and opencv-2. OpenCV is an open source software library that allows developers to access routines in API (Application Programming Interface) used for computer vision applications. The code above assigns a label to each image that is to recognized. The good news is that you no longer have to. ) that you can use (though no source code available). Extract text with OCR for all image types in python using pytesseract. More than a HOWTO, this document is a HOW-DO-I use Python to do my image processing tasks. Python-tesseract is a wrapper for Google's Tesseract-OCR Engine. To read the subtitles out of my images, Google search led me to Tesseract. In this tutorial, you will learn how you can process images in Python using the OpenCV library. The architecture of a CNN is designed to take advantage of the 2D structure of an input image (or other 2D input such as a speech signal).