Penac writing a letter

In the article, they argued that American conservatives were "adrift" in the area of foreign policy, advocated a "more elevated vision of America's international role," and suggested that the United States' should adopt a stance of "benevolent global hegemony. The statement had 25 signers, including project members and outside supporters see Signatories to Statement of Principles. It described the United States as the "world's pre-eminent power," and said that the nation faced a challenge to "shape a new century favorable to American principles and interests.

Penac writing a letter

Our system, which uses pen-trajectory information, is suitable for use in pen-based computers. It has a multi-modular architecture whose central trainable module is a Time Delay Neural Network.

Results comparing our system and a commercial recognizer are presented. Our best recognizer make three times less errors on hand-printed word recognition than the commercial one. By on-line we mean that the input to the system is the pen trajectory, sampled at regular time intervals by a touch sensitive pad.

Weissman iii this article, we provide a synthesis of previously published work from our group [1, 2, 3, 4, 5, 6, 7] and a perspective on the on-going research. There is urgent need for good recognizers to ensure the success of the rst pen-based computers and pen-based personal communicators.

In spite of the e orts of many companies and universities, the state-of-the-art on- line handwriting recognition accuracy has not yet reached a level which is acceptable to users. We tackle this problem from di erent angles.

Second, we introduce writer adaptation to ne tune the recognizer with examples of a particular user style. The originality of using Neural Networks, and perhaps also the main advantage, is that the network itself can be decomposed into two modules [9]: We make extensive use of our neural feature extractor which is a trainable module capable of producing a very good and compact feature representa- tion.

It is a convolutional network which has several layers of local feature extractors. It is the one-dimensional version of the network used by the Optical Character Recognition group in our department [12, 13, 14] and is suitable for processing time varying signals, such as the pen-trajectory.

Preprocessing techniques incorporate human knowledge about the task at hand, such as known invariances and relevant features. In this work, we use a rather crude preprocessing and rely mostly on the neural network to enforce invariances and extract features.

Our preprocessing consists of fairly simple normalizations and extraction of low level local topological features such as line orientation. Because the input data is the pen-trajectory, we face the choice of whether or not to use the dynamic information.

It is possible get rid of the temporal parametrization of the data and represent patterns as pixel images. In this work, however, we encode patterns as a sequence of feature vectors, corresponding to the sequence of iv 1.

The feature vector in this case has 8 components encoding x and y coordinates normalized and rescaled, pen up or down information, speed of the pen, two components for direction of the pen trajectory and two components for its curvature.

The preprocessing is decomposed into normalization centering, scaling, deskewingresampling to obtain a desired number of regularly spaced points along the trajectorysmoothing to get rid of jittering and feature extraction.

In the resulting representation, each point on the tra- jectory is associated with a feature vector whose components are a subset of: Depending on the application, the preprocessing may slightly vary. For instance, it is debatable whether the representation should be invariant under changes in the speed of the pen.

For writer independent character recognition, variations in the speed of the pen are a nuisance. Conversely, for signature veri cation, the exact dynamics of drawing actions are very precious for the detection of forgeries. Data collection devices sample the trajectory at regular time intervals milliseconds.

Some invariance with respect to the speed of the pen is obtained, for character recognition applications, by resampling to points regularly spaced in arc-length, as op- posed to regularly spaced in time. For signature veri cation, the resampling preserves regular time intervals.

The neural feature extractor in Fig. T DNN 's were rst introduced for speech recognition and are well suited to sequential signal processing [10, 11].

One layer of the network transforms a sequence of feature vectors into another sequence of higher order feature vectors in the following way.

A given neuron detects a particular local topological feature of the pen- trajectory. Its receptive eld is restricted to a limited time window. The same neuron is reused along the time axis Fig. By using several dif- ferent neurons at each time step, the neural network performs the detection of di erent features Fig.

The operations performed by one layer of the network are convolutional in essence Fig. State sequences are parametrized by the discrete time vari- able t. The con dence levels are squashed by the neuron sigmoid function g[.

The time component of our input representation is gradually eliminated by subsampling the convolution at each layer by a factor of two or three. To partially compensate for the loss of information, the number of features is gradually increased [15].

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This is what we call a bi-pyramidal network architecture. Bi-pyramidal networks convert progressively time information into feature information. The weights are adjusted during a supervised training session, using error back-propagation [16] which performs gradient descent in weight space with a mean-squared-error MSE loss function see Sect.Year after year, our Slimline pens are our top selling writing instruments.

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penac writing a letter

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penac writing a letter

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