Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Intelligent Automated Assistant

Pattern recognition

A virtual assistant uses context information to supplement natural language or gestural input from a user. Context helps to clarify the user's intent and to reduce the number of candidate interpretations of the user's input, and reduces the need for the user to provide excessive clarification input. Context can include any available information that is usable by the assistant to supplement explicit user input to constrain an information-processing problem and/or to personalize results. Context can be used to constrain solutions during various phases of processing, including, for example, speech recognition, natural language processing, task flow processing, and dialog generation.

Using context information to facilitate processing of commands in a virtual assistant

A method and apparatus are provided for building an intelligent automated assistant. Embodiments of the present invention rely on the concept of “active ontologies” (e.g., execution environments constructed in an ontology-like manner) to build and run applications for use by intelligent automated assistants. In one specific embodiment, a method for building an automated assistant includes interfacing a service-oriented architecture that includes a plurality of remote services to an active ontology, where the active ontology includes at least one active processing element that models a domain. At least one of the remote services is then registered for use in the domain.

Method and apparatus for building an intelligent automated assistant

Most of the past document image watermarking schemes focus on providing same level of integrity and copyright protection for information present in the source document image. However, in a document image the information contents possess various levels of sensitivity. Each level of sensitivity needs different type of protection and this demands multiple watermarking techniques. In this paper, a novel intelligent multiple watermarking techniques are proposed. The sensitivity of the information content of a block is based on the homogeneity and relative energy contribution parameters. Appropriate watermarking scheme is applied based on sensitivity classification of the block. Experiments are conducted exhaustively on documents. Experimental results reveal the accurate identification of the sensitivity of information content in the block. The results reveal that multiple watermarking schemes has reduced the amount of data to be embedded and consequently improved perceptual quality of the watermarked image.

Computer Vision, Graphics, and Image Processing

A computer based method of biometric analysis which compares a first vector from a first biometric sample with a second vector from a second biometric sample. The vectors have at least one biometric feature. A method which compares two biometric samples. The samples form at least one cluster of at least one vector based on feature similarities between the samples. An apparatus incorporating a means for performing the method taught herein. A computer-readable medium and a propagated computer data signal transmitted via a propagation medium, with instructions which when executed by a processor, carry out the method of the present invention. A method of handwriting analysis which calculates a first metric from a first vector having at least one feature from a sample, calculates a second metric from a second vector having at least one feature from a second sample, and calculates the distance in two-dimensional feature space between metrics.

Method and apparatus for analyzing and/or comparing handwritten and/or biometric samples

In this paper, we present a new text line extraction method for handwritten Arabic documents. The proposed technique is based on a generalized adaptive local connectivity map (ALCM) using a steerable directional filter. The algorithm is designed to solve the particularly complex problems seen in handwritten documents such as fluctuating, touching or crossing text lines. The proposed algorithm consists of three steps. Firstly, a steerable filter is used to probe and determine foreground intensity along multiple directions at each pixel while generating the ALCM. The ALCM is then binarized using an adaptive thresholding algorithm to get a rough estimate of the location of the text lines. In the second step, connected component analysis is used to classify text and non text patterns in the generated ALCM to refine the location of the text lines. Finally, the text lines are separated by superimposing the text line patterns in the ALCM on the original document image and extracting the connected components covered by the pattern mask. Analysis of experimental results on the DARPA MADCAT Arabic handwritten document data indicate that the method is robust and is capable of correctly isolating handwritten text lines even on challenging document images.

http://www.cvc.uab.es/icdar2009/papers/3725a176.pdf

A Steerable Directional Local Profile Technique for Extraction of Handwritten Arabic Text Lines

This paper presents an algorithm using adaptive local connectivity map for retrieving text lines from the complex handwritten documents such as handwritten historical manuscripts. The algorithm is designed for solving the particularly complex problems seen in handwritten documents. These problems include fluctuating text lines, touching or crossing text lines and low quality image that do not lend themselves easily to binarizations. The algorithm is based on connectivity features similar to local projection profiles, which can be directly extracted from gray scale images. The proposed technique is robust and has been tested on a set of complex historical handwritten documents such as Newton's and Galileo's manuscripts. A preliminary testing shows a successful location rate of above 95% for the test set.

Text extraction from gray scale historical document images using adaptive local connectivity map

Research on OCR of Indian scripts is gaining momentum in recent times. Many projects funded by government and industry are currently underway to scan hundreds of thousands of indic-script documents and manuscripts to create large digital library archives to preserve these treasures for posterity. OCR is a key enabling technology for making these archives practically accessible to researchers and lay users alike by creating search-able indexes and machine readable text repositories of these documents. This book provides an overview of the current state-of-the–art in the OCR of the different Indic scripts as well as other issues in the creation of accessible digital libraries for Indic scripts. It provides a good technical overview of the latest research in the field.

Srirangaraj Setlur

Papers

Computer Vision, Graphics, and Image Processing

Method and apparatus for analyzing and/or comparing handwritten and/or biometric samples

A Steerable Directional Local Profile Technique for Extraction of Handwritten Arabic Text Lines

Text extraction from gray scale historical document images using adaptive local connectivity map

Guide to OCR for Indic Scripts