AI, Computer Vision, Algorithms and Mathematics
In character recognition, the distance between two edges of a stroke, measured perpendicular to the stroke centerline is called the stroke width. Take a look at the below image. The length of lines marked in Red gives the stroke width of the character. There are quite a few ways to identify the stroke width, In […]
In this post I will be talking about how we can use Hough Transform to detect and correct Skewness of a document image. There have been many research papers published around this problem and it keeps getting published even today on various journals mainly because its still largely an unsolved problem. I had previously written […]
One of the important steps in OCR is the thresholding process. It helps us in separating the text regions (foreground) from the background. If you apply a thresholding algorithm like OTSU or Sauvola, you might end up with a lot of noise. Some of your text regions may even get categorized as background. Take a […]
This is for my reference and this might come in handy for others too. All Tesseract options CLI Examples Command Example Notes tesseract sample_images/image2.jpg stdout To print the output to standard output tesseract sample_images/image2.jpg sample_images/output By default the output will be named outbase.txt. tesseract sample_images/image2.jpg sample_images/output -l eng -l is for language. English is default […]
The problem of building systems that mimic human behavior is not an easy one to solve. Neural Nets solve those problems in one way but if someone thinks that it’s the right way doesn’t know enough about Artificial Intelligence. When you show a child a picture of a dog, just one picture, the child can […]
The process of separating the foreground pixels from the background is called thresholding. There are many ways of achieving optimal thresholding and one of the ways is called the Otsu’s method, proposed by Nobuyuki Otsu. Otsu’s method[1] is a variance-based technique to find the threshold value where the weighted variance between the foreground and background […]
Convex Hull is one of the fundamental algorithms in Computational geometry used in many computer vision applications like Collision avoidance in Self Driving Cars, Shape analysis and Hand Gesture-recognition, etc. By Definition, A Convex Hull is the smallest convex set that encloses a given set of points. For Example, Given a set of points P […]
Abstract This article presents a method for automatic detection and extraction of number plates from the images of cars. There are usually three steps in an Automatic Number Plate Recognition (ANPR) system. The first one is to binarize the image and separate the background from the foreground. The Foreground contains the numbers of the number […]
This article presents a method for reducing the number of colors in an image using K-means clustering. This is a continuation of my previously posted color quantization using Uniform Quantization and Median Cut Quantization. K-Means is one of the simplest unsupervised clustering algorithm used to cluster data into K clusters. The algorithm iteratively assigns the […]
In my previous post, I talked about the Uniform Quantization algorithm, which was a simple way of reducing the colors in the image. Though it’s easy to implement, it doesn’t always yield good results when there are many colors belonging to the same region. Also, a lot of color regions will not have any colors […]