Adding feedback to improve segmentation and recognition of handwritten numerals

Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.Includes bibliographical references (leaves 68-69).by Susan A. Dey.S.B.and M.Eng

A simple generic attack on text captchas

Text-based Captchas have been widely deployed across the Internet to defend against undesirable or malicious bot programs. Many attacks have been proposed; these fine prior art advanced the scientific understanding of Captcha robustness, but most of them have a limited applicability. In this paper, we report a simple, low-cost but powerful attack that effectively breaks a wide range of text Captchas with distinct design features, including those deployed by Google, Microsoft, Yahoo!, Amazon and other Internet giants. For all the schemes, our attack achieved a success rate ranging from 5% to 77%, and achieved an average speed of solving a puzzle in less than 15 seconds on a standard desktop computer (with a 3.3GHz Intel Core i3 CPU and 2 GB RAM). This is to date the simplest generic attack on text Captchas. Our attack is based on Log-Gabor filters; a famed application of Gabor filters in computer security is John Daugman’s iris recognition algorithm. Our work is the first to apply Gabor filters for breaking Captchas

An Overview of Advances of Pattern Recognition Systems in Computer Vision

26 pagesFirst of all, let's give a tentative answer to the following question: what is pattern recognition (PR)? Among all the possible existing answers, that which we consider being the best adapted to the situation and to the concern of this chapter is: "pattern recognition is the scientific discipline of machine learning (or artificial intelligence) that aims at classifying data (patterns) into a number of categories or classes". But what is a pattern? A pattern recognition system (PRS) is an automatic system that aims at classifying the input pattern into a specific class. It proceeds into two successive tasks: (1) the analysis (or description) that extracts the characteristics from the pattern being studied and (2) the classification (or recognition) that enables us to recognise an object (or a pattern) by using some characteristics derived from the first task

MINHLP: Module to Identify New Hampshire License Plates

A license plate, referred to simply as a plate or vehicle registration plate, is a small plastic or metal plate attached to a motor vehicle for official identification purposes. Most governments require a registration plate to be attached to both the front and rear of a vehicle, although certain jurisdictions or vehicle types, such as motorcycles, require only one plate, which is usually attached to the rear of the vehicle. We present analysis of Automatic License Plate Recognition (ALPR) of New Hampshire (NH) plates using open source products. This thesis contains an implementation of a demonstrated model and analysis of the results. In this paper, OpenCV (computer vision library) and Tesseract (open source optical character reader) is presented as a core intelligent infrastructure. The thesis explains the mathematical principles and algorithms used for number plate detection, processes of proper characters segmentation, normalization and recognition. A description of the challenges involved in detecting and reading license plate in NH, previous studies done by others and the strategies adopted to solve them is also given

Advances in Character Recognition

This book presents advances in character recognition, and it consists of 12 chapters that cover wide range of topics on different aspects of character recognition. Hopefully, this book will serve as a reference source for academic research, for professionals working in the character recognition field and for all interested in the subject

Detecting Multilingual Lines of Text with Fusion Moves

This thesis proposes an optimization-based algorithm for detecting lines of text in images taken by hand-held cameras. The majority of existing methods for this problem assume alphabet-based texts (e.g. in Latin or Greek) and they use heuristics specific to such texts: proximity between letters within one line, larger distance between separate lines, etc. We are interested in a more challenging problem where images combine alphabet and logographic characters from multiple languages where typographic rules vary a lot (e.g. English, Korean, and Chinese). Significantly higher complexity of fitting multiple lines of text in different languages calls for an energy-based formulation combining a data fidelity term and a regularization prior. Our data cost combines geometric errors and likelihoods given by a classifier trained to low-level features in each language. Our regularization term encourages sparsity based on label costs. Our energy can be efficiently minimized by fusion moves. The algorithm was evaluated on a database of images from the subway of metropolitan area of Seoul and was proven to be robust

Oversegmentation methods for character segmentation in off-line cursive handwritten word recognition : an overview

Character segmentation (i.e., splitting the images of handwritten words into pieces corresponding to single letters) is one of the required steps in numerous off-line cursive handwritten word recognition solutions. It is also a very important step, because improperly extracted characters are usually impossible to recognize correctly with currently used methods. The most common method of character segmentation is initial oversegmentation – finding some set of potential splitting points in the graphical representation of the word and then attempting to eliminate the improper ones. This paper contains a list of popular approaches for generating potential splitting points and methods of verifying their correctness