An Integrated architecture for recognition of totally unconstrained handwritten numerals

Reprint. Reprinted from the International journal of pattern recognition and artificial intelligence. Vol. 7, no. 4 (1993) "January 1993."Includes bibliographical references (p. 127-128).Supported by the Productivity From Information Technology (PROFIT) Research Initiative at MIT.Amar Gupta ... [et al.

Integration of traditional imaging, expert systems, and neural network techniques for enhanced recognition of handwritten information

Includes bibliographical references (p. 33-37).Research supported by the I.F.S.R.C. at M.I.T.Amar Gupta, John Riordan, Evelyn Roman

Handwritten numerical recognition based on multiple algorithms

In this paper, the authors combine two algorithms for application to the recognition of unconstrained isolated handwritten numerals. The first algorithm employs a modified quadratic discriminant function utilizing direction sensitive spatial features of the numeral image. The second algorithm utilizes features derived from the profile of the character in a structural configuration to recognize the numerals. While both algorithms yield very low error rates, the authors combine the two algorithms in different ways to study the best polling strategy and realize very low error rates (0.2% or less) and rejection rates below 4%.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/29653/1/0000742.pd

Deep Learning Based Real Time Devanagari Character Recognition

The revolutionization of the technology behind optical character recognition (OCR) has helped it to become one of those technologies that have found plenty of uses in the entire industrial space. Today, the OCR is available for several languages and have the capability to recognize the characters in real time, but there are some languages for which this technology has not developed much. All these advancements have been possible because of the introduction of concepts like artificial intelligence and deep learning. Deep Neural Networks have proven to be the best choice when it comes to a task involving recognition. There are many algorithms and models that can be used for this purpose. This project tries to implement and optimize a deep learning-based model which will be able to recognize Devanagari script’s characters in real time by analyzing the hand movements

A Computational Theory of Contextual Knowledge in Machine Reading

Machine recognition of off–line handwriting can be achieved by either recognising words as individual symbols (word level recognition) or by segmenting a word into parts, usually letters, and classifying those parts (letter level recognition). Whichever method is used, current handwriting recognition systems cannot overcome the inherent ambiguity in writingwithout recourse to contextual information. This thesis presents a set of experiments that use Hidden Markov Models of language to resolve ambiguity in the classification process. It goes on to describe an algorithm designed to recognise a document written by a single–author and to improve recognition by adaptingto the writing style and learning new words. Learning and adaptation is achieved by reading the document over several iterations. The algorithm is designed to incorporate contextual processing, adaptation to modify the shape of known words and learning of new words within a constrained dictionary. Adaptation occurs when a word that has previously been trained in the classifier is recognised at either the word or letter level and the word image is used to modify the classifier. Learning occurs when a new word that has not been in the training set is recognised at the letter level and is subsequently added to the classifier. Words and letters are recognised using a nearest neighbour classifier and used features based on the two–dimensional Fourier transform. By incorporating a measure of confidence based on the distribution of training points around an exemplar, adaptation and learning is constrained to only occur when a word is confidently classified. The algorithm was implemented and tested with a dictionary of 1000 words. Results show that adaptation of the letter classifier improved recognition on average by 3.9% with only 1.6% at the whole word level. Two experiments were carried out to evaluate the learning in the system. It was found that learning accounted for little improvement in the classification results and also that learning new words was prone to misclassifications being propagated

SISTEMA DE RECONOCIMIENTO DE DÍGITOS MANUSCRITOS UTILIZANDO REDES NEURONALES

El reconocimiento de dígitos manuscritos es un entorno en creciente uso y por consiguiente requiere ir abordando alternativas para su implementación, el  uso de redes neuronales ha venido retomando el auge dentro del área de reconocimiento de patrones. Este documento muestra el uso de redes neuronales, a través de un software personalizado, como el motor detrás un sistema de reconocimiento de caracteres ópticos. En este sistema los dígitos numéricos son simplificados a través de filtros de imagen y luego presentados como entrada a la red neuronal para entrenarla (usando el algoritmo de retro-propagación) y ser capaz de clasificar otras muestras en la etapa de pruebas. Los resultados muestran tasas de reconocimiento cercanas al 85%, que se pueden considerar como aceptables para topologías de una sola capa, dejando pendiente para futuros experimentos el trabajo con redes multicapa pre-entrenadas, ya que suelen incrementar fuertemente su eficiencia

Semantic representation of digital ink in the classroom learning partner

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (p. 59-63).The research presented in this thesis addresses a critical issue in the introduction of new tablet-PC-based educational technology in the classroom: interpretation and semantic representation of digital ink. The teaching paradigm being investigated is that of in-class exercises, which have proven beneficial to student learning. Recent advances in educational technology support such exercises by means of distributed wireless presentation systems. Such systems have proven successful, but are limited in their scope because they rely on digital ink as the communication medium between an instructor and his or her students. The work done in this thesis extends the use of such systems and makes the following contributions towards the creation of a learning partner in the classroom: * an ink interpreter capable of text and arrow interpretation which can rival PRS on multiple choice and true-false questions * a framework for sketch and text interpretation inspired from state of the art research in handwriting and sketch interpretation * an infrastructure necessary for creating in-class exercises as part of Classroom Presenter, interpreting student answers, and aggregating them.by Michel A. Rbeiz.M.Eng

Building E-education platform for design-oriented learning

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2004.Includes bibliographical references (p. 149-155).Design-oriented learning requires tools that support creative processes and student-to-student and student-to-faculty interactions. While most present E-Education systems perform as the asynchronous distribution channel for teaching material, they usually offer little support for project based design processes. This research maps out the key learning events in design classes at MIT's Department of Mechanical Engineering, and proposes guidelines for building E-Education systems to support the unique characteristics of design-oriented learning. Two creative learning processes are identified and two independent, yet tightly related, software systems are implemented and evaluated. The first application, the Peer Review and Engineering Process (PREP), is a web system that helps instructors and students conduct and manage peer review evaluation of design concepts. The second is a real time application called InkBoard that leverages the Tablet PC and Ink medium to provide real-time collaborative sketching over TCP/IP networks. A new streaming network protocol for transferring Ink objects is proposed and implemented. A comparative study against other ink-enabled protocols is also presented.by Hai Ning.Ph.D

Multistable Perception, False Consensus, and Information Complements

This paper presents a distributed communication model to investigate multistable perception, where a stimulus gives rise to multiple competing perceptual interpretations. We formalize stable perception as consensus achieved through components exchanging information. Our key finding is that relationships between components influence monostable versus multistable perceptions. When components contain substitute information about the prediction target, stimuli display monostability. With complementary information, multistability arises. We then analyze phenomena like order effects and switching costs. Finally, we provide two additional perspectives. An optimization perspective balances accuracy and communication costs, relating stability to local optima. A Prediction market perspective highlights the strategic behaviors of neural coordination and provides insights into phenomena like rivalry, inhibition, and mental disorders. The two perspectives demonstrate how relationships among components influence perception costs, and impact competition and coordination behaviors in neural dynamics