Computer Vision

HTR

• MNSIT: Recognizing the numbers (Part 2) - In this second part on MNSIT data, we will see how to edit and prepare the data to reach 97%.
• MNSIT: Recognizing the numbers (Part 1) - Let's get started with number recognition with the well-known MNSIT dataset!

OCR

• Straighten a poorly scanned text document with deskew - In this article I show you how in a few lines of Python code you can straighten a document that has for example been scanned crooked.
• KYC check with Blue Prism Decipher IDP - Discover in a short demo how Blur Prism Decipher IDP allows you to easily automate the classification and then the extraction of data from unstructured documents (scanned for example).
• ID Card analysis - In this article we will see how we will be able to recover information (photo, and other information) from a scanned identity card. In this article we will use the OpenCV and tesseract libraries with Python.
• Advanced use of Tesseract with Python - Find out in this article how to use tesseract in relatively complex cases with Python and OpenCV.
• A REST service that processes invoices scanned in Python - In this post, I suggest you create a web service (RESTFul) in Python which will retrieve an image (JPEG) to analyze and process it in order to return the constituent elements.
• Get started with Tesseract - 122/5000 Interested in OCRs? learn how to use Tesseract (Open Source OCR) from the command line but also via Python.

Form Recognition

• YOLO (Part 3) Non Maxima Suppression (NMS) - In my previous articles on YOLO we saw how to use this network ... but when we apply this algorithm on complex images we quickly see that multiple detections are made for the same objects. We will see in this article how to remove these duplicate frames with the so-called NMS technique.
• YOLO (Part 2) Object detection with YOLO & OpenCV - In this article we will see step by step how to use the YOLO neural network with its implementation in OpenCV. Follow the guide 😉
• YOLO (Part 1) Introduction with Darknet - We will see in this article, how with the YOLO neural network we can very simply detect several objects in a photo. The objective is not to go into the details of the implementation of this neural network (much more complex than a simple sequential CNN) but rather to show how to use the implementation which was carried out in C ++ and which is called Darknet.
• Image processing (part 7) Convolution Neural Networks – CNN - In this article we will see step by step how to create and use a convolutional neural network (CNN) to classify images.
• Facial recognition in video with OpenCV - In this post you'll figure out how it's easy to make facial recognition inside a video flow with OpenCV.
• Automate image straightening with Blue Prism and Python - In this article we will see how to automate image straightening with blue Prism and Python.
• Straighten a poorly scanned text document with deskew - In this article I show you how in a few lines of Python code you can straighten a document that has for example been scanned crooked.
• ID Card analysis - In this article we will see how we will be able to recover information (photo, and other information) from a scanned identity card. In this article we will use the OpenCV and tesseract libraries with Python.
• Facial recognition in an image with OpenCV - Find out in this article how to use OpenCV to easily do facial recognition in a image.

Images processing

• YOLO (Part 4) Reduce detected classes - In this article we will see a trick that reduces the scope of object detection using YOLO v4.
• YOLO (Part 3) Non Maxima Suppression (NMS) - In my previous articles on YOLO we saw how to use this network ... but when we apply this algorithm on complex images we quickly see that multiple detections are made for the same objects. We will see in this article how to remove these duplicate frames with the so-called NMS technique.
• YOLO (Part 2) Object detection with YOLO & OpenCV - In this article we will see step by step how to use the YOLO neural network with its implementation in OpenCV. Follow the guide 😉
• YOLO (Part 1) Introduction with Darknet - We will see in this article, how with the YOLO neural network we can very simply detect several objects in a photo. The objective is not to go into the details of the implementation of this neural network (much more complex than a simple sequential CNN) but rather to show how to use the implementation which was carried out in C ++ and which is called Darknet.
• Transfer Learning with VGG - In this article we will discuss the concept of Transfer Learning ... or how to avoid redoing long and consuming learning by partially reusing a pre-trained neural network. To do this we will use a network which is the reference in the matter: VGG-Net (vgg16).
• Image processing (part 7) Convolution Neural Networks – CNN - In this article we will see step by step how to create and use a convolutional neural network (CNN) to classify images.
• Image processing (part 6) Filters & Convolution - We will discuss in this post a kind of filters widely used by all images software (such as Photoshop or Gimp). In fact and to go further (without "sploiling" the following posts either) this convolution principle will also be widely used by neural networks (Deep Learning) ... but we will see that later. First of all, let's focus on the principle of these convolution filters.
• Image processing (part 5) Morphologic Transformations - We will see in this article the principles of erosion and dilation of images which are widely used especially during the restoration of poor quality images.
• Image processing (part 4) Image Transformations - We will see in this article how to perform some basic transformations on images with scikit-image such as rotating, and changing image scale and size.
• Image processing (part 3) Image Thresholding - In this article (which is the 3rd episode of the image processing series) we will see how to use the image histograms we discussed in article 2 to do some basic editing.