Computer Vision

Skew Detection and Correction of Document images using Hough Transform

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 …

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All Tesseract OCR options

This is for my reference and this might come in handy for others too. All Tesseract options $ tesseract –help-extra 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. …

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Otsu’s method for image thresholding explained and implemented

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 …

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convex hull

Understanding Graham scan algorithm for finding the Convex hull of a set of Points

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 …

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text like regions

Algorithm for detecting and extracting number plates from images of cars

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 …

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Reduce the number of Colors of an image using K-Means Clustering

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 …

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original non quantized

Reduce the number of Colors of an image using Uniform Quantization

Reducing the number of colors in an image is also called Color quantization. It’s commonly used for generating GIF images which currently supports only 256 colors. The general idea is, group similar colors in an image into regions, replace them with the color which closely resembles or represents the region. This color is also called …

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