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Description of a writing console which was used for monitoring handwriting behavior. The main feature of the console is a translucent Plexiglass paddle, pivoted on a thin bronze tube, with its top flat surface providing the writing surface. The console was used in experiments on two subjects under various schedules of monetary reinforcement for handwriting. The results suggest that handwriting is an effective approach to the analysis of human behavior.
Pattern recognition of Chinese handwritten characters
The Apple(R) Newton(TM) MessagePad 110 was flown aboard the KC-135 reduced gravity aircraft for microgravity usability testing. The Newton served as the initial hand-held electronic logbook prototype for the International Space Station (ISS) Human Research Facility (HRF). Subjects performed three different tasks with the Newton: (1) using the stylus to tap on different sections of the screen in order to launch an application and to select options within it; (2) using the stylus to write, and; (3) correcting handwriting recognition errors in a handwriting-intensive application. Subjects rated handwriting in microgravity 'Borderline' and had great difficulties finding a way in which to adequately restrain themselves at the lower body in order to have their hands free for the Newton. Handwriting recognition was rated 'Unacceptable,' but this issue is hardware-related and not unique to the microgravity environment. It is suggested that the restraint and handwriting issues are related and require further joint research with the current Handheld Electronic Logbook prototype: the Norand Pen*key Model #6300.
Machine recognition of handwriting patterns treated as two-dimensional vector displacements in time
With the increasing number of data-driven models in nuclear applications, large volumes of numerical data are required to accurately model and predict the health status of a plant component. However, many historical operation logs that contain useful information are not fully utilized due to the lack of a systematic approach of digitization. To overcome this issue, this study proposes an automatic pipeline for extracting information from handwritten tabular documents collected from nuclear power plants. In our pipeline, we first denoise scanned documents with morphological operations, and then extract relevant parts from individual pages using both traditional computer vision and neural network methods. Handwriting recognition is applied to obtain text and numbers. As the most challenging step is how to crop only relevant information, the main focus of our paper is to detect tables and cells from scanned handwritten documents. Here we evaluate the efficiency and accuracy of our proposed method on handwritten operational reports obtained from a real-world case study. The results demonstrate the high accuracy and practicality of our proposed method.
Rapid advances in deep learning have led to paradigm shifts in a number of fields, from medical image analysis to autonomous systems. These advances, however, have resulted in digital neural networks with large computational requirements, resulting in high energy consumption and limitations in real-time decision-making when computation resources are limited. Here, we demonstrate a meta-optic–based neural network accelerator that can off-load computationally expensive convolution operations into high-speed and low-power optics. In this architecture, metasurfaces enable both spatial multiplexing and additional information channels, such as polarization, in object classification. End-to-end design is used to co-optimize the optical and digital systems, resulting in a robust classifier that achieves 93.1% accurate classification of handwriting digits and 93.8% accuracy in classifying both the digit and its polarization state. This approach could enable compact, high-speed, and low-power image and information processing systems for a wide range of applications in machine vision and artificial intelligence.
Perception and recognition with respect to communications theory - handwriting simulation
Two-man teams solved credible, 'real-world' problems for which computer assistance has been or could be useful. Conversations were carried on in one of four modes of communication: (1) typewriting, (2) handwriting, (3) voice, and (4) natural, unrestricted communication. Two groups of subjects (experienced and inexperienced typists) were tested in the typewriting mode. Performance was assessed on three classes of dependent measures: time to solution, behavioral measures of activity, and linguistic measures. Significant and meaningful differences among the communication modes were found in each of the three classes of dependent variable. This paper is concerned mainly with the results of the activity analyses. Behavior was recorded in 15 different categories. The analyses of variance yielded 34 statistically significant terms of which 27 were judged to be practically significant as well. When the data were transformed to eliminate heterogeneity, the analyses of variance yielded 35 statistically significant terms of which 26 were judged to be practically significant.
Two-man teams solved credible, 'real world' problems for which computer assistance has been or could be useful. Conversations were carried on in one of four modes of communication: typewriting, handwriting, voice, and natural unrestricted communication. Performance was assessed on three classes of dependent measures: time to solution, behavioral measures of activity, and linguistic measures. Significant differences among the communication modes were found in each of the three classes. This paper is concerned mainly with the results of the linguistic analyses. Linguistic performance was assessed with 182 measures, most of which turned out to be redundant and some of which were useless or meaningless. Those that remain show that although problems can be solved faster in the oral modes than in the hard-copy modes, the oral modes are characterized by many more messages, sentences, words, and unique words; much higher communication rates; but lower type-token ratios. Although a number of significant problem and job-role effects were found, there were relatively few significant interactions of modes with thsse variables. It appears, therefore, that the mode effects hold for both problems and for both job roles assigned to the subjects.
Spacecraft operations including ground system operations are generally realized by various large or small scale group work which is done by operators, engineers, managers, users and so on, and their positions are geographically distributed in many cases. In face-to-face work environments, it is easy for them to understand each other. However, in distributed work environments which need communication media, if only using audio, they become estranged from each other and lose interest in and continuity of work. It is an obstacle to smooth operation of spacecraft. NASDA has developed an experimental model of a new real-time operation control system called 'ADOLIS' (ADvanced On-Line System) adopted to such a distributed environment using a multi-media system dealing with character, figure, image, handwriting, video and audio information which is accommodated to operation systems of a wide range including spacecraft and ground systems. This paper describes the results of the development of the experimental model.
Irvine Sensors Corporation (ISC), working closely with JPL under BMDO/ONR sponsorship, is developing a radically new neural computing technology. Primarily aimed at discrimination and target recognition for BMDO missile interceptor applications, it appears to have near term commercial applicability to such problems as handwriting and face recognition, just to name two. In its earliest form it will be able to perform inner product computation using 262 thousand 64x64 templates (weighted synapse arrays) where the 64(exp 5) weights can all be changed every millisecond. Internal switching provides an inherent capability to zoom, translate, or rotate the templates. The 3D silicon architecture is manufactured on a commercial, high volume DRAM production line at very low cost, enabling its commercialization. Two technology thrusts are beginning: in the first, the 64 layer capability of 3DANN-I will be extended to 1024 layers and beyond. In the second layer size will be shrunk to 2-3 millimeters to reduce layer costs. Our workshop goal is to expose this technology to the neural network community as an emerging tool for their use and to obtain their desire for its future development.
Artificial neural network models can make amazing computations. These models are explained along with their application in problems associated with fighting crime. Specific problems addressed are identification of people using face recognition, speaker identification, and fingerprint and handwriting analysis (biometric authentication).
A recently developed algorithm performs kernel-machine classification via incremental approximate nearest support vectors. The algorithm implements support-vector machines (SVMs) at speeds 10 to 100 times those attainable by use of conventional SVM algorithms. The algorithm offers potential benefits for classification of images, recognition of speech, recognition of handwriting, and diverse other applications in which there are requirements to discern patterns in large sets of data. SVMs constitute a subset of kernel machines (KMs), which have become popular as models for machine learning and, more specifically, for automated classification of input data on the basis of labeled training data. While similar in many ways to k-nearest-neighbors (k-NN) models and artificial neural networks (ANNs), SVMs tend to be more accurate. Using representations that scale only linearly in the numbers of training examples, while exploring nonlinear (kernelized) feature spaces that are exponentially larger than the original input dimensionality, KMs elegantly and practically overcome the classic curse of dimensionality. However, the price that one must pay for the power of KMs is that query-time complexity scales linearly with the number of training examples, making KMs often orders of magnitude more computationally expensive than are ANNs, decision trees, and other popular machine learning alternatives. The present algorithm treats an SVM classifier as a special form of a k-NN. The algorithm is based partly on an empirical observation that one can often achieve the same classification as that of an exact KM by using only small fraction of the nearest support vectors (SVs) of a query. The exact KM output is a weighted sum over the kernel values between the query and the SVs. In this algorithm, the KM output is approximated with a k-NN classifier, the output of which is a weighted sum only over the kernel values involving k selected SVs. Before query time, there are gathered statistics about how misleading the output of the k-NN model can be, relative to the outputs of the exact KM for a representative set of examples, for each possible k from 1 to the total number of SVs. From these statistics, there are derived upper and lower thresholds for each step k. These thresholds identify output levels for which the particular variant of the k-NN model already leans so strongly positively or negatively that a reversal in sign is unlikely, given the weaker SV neighbors still remaining. At query time, the partial output of each query is incrementally updated, stopping as soon as it exceeds the predetermined statistical thresholds of the current step. For an easy query, stopping can occur as early as step k = 1. For more difficult queries, stopping might not occur until nearly all SVs are touched. A key empirical observation is that this approach can tolerate very approximate nearest-neighbor orderings. In experiments, SVs and queries were projected to a subspace comprising the top few principal- component dimensions and neighbor orderings were computed in that subspace. This approach ensured that the overhead of the nearest-neighbor computations was insignificant, relative to that of the exact KM computation.
Computer recognition of handwritten words
Computer recognition of connected handwritten words by stroke decomposition