1,015 research outputs found
Study and Observation of the Variations of Accuracies for Handwritten Digits Recognition with Various Hidden Layers and Epochs using Neural Network Algorithm
In recent days, Artificial Neural Network (ANN) can be applied to a vast
majority of fields including business, medicine, engineering, etc. The most
popular areas where ANN is employed nowadays are pattern and sequence
recognition, novelty detection, character recognition, regression analysis,
speech recognition, image compression, stock market prediction, Electronic
nose, security, loan applications, data processing, robotics, and control. The
benefits associated with its broad applications leads to increasing popularity
of ANN in the era of 21st Century. ANN confers many benefits such as organic
learning, nonlinear data processing, fault tolerance, and self-repairing
compared to other conventional approaches. The primary objective of this paper
is to analyze the influence of the hidden layers of a neural network over the
overall performance of the network. To demonstrate this influence, we applied
neural network with different layers on the MNIST dataset. Also, another goal
is to observe the variations of accuracies of ANN for different numbers of
hidden layers and epochs and to compare and contrast among them.Comment: To be published in the 4th IEEE International Conference on
Electrical Engineering and Information & Communication Technology (iCEEiCT
2018
The Implementation of Feedforward Backpropagation Algorithm for Digit Handwritten Recognition in a Xilinx Spartan-3
This research is aimed to implement feedforward backpropagation algorithm for digit handwritten recognition in an FPGA, Xilinx Spartan 3. This research is expected to give a contribution such as the feedforward algorithm design in VLSI technology based on FPGA, the practice module of Xilinx Spartan-3 development board and further research in artificial neural network and FPGA field in Electronics Laboratory.The feedforward backpropagation algorithm is used to recognize 10 objects. The feedforward backpropagation network consists of two layers, 36 input unit which is the feature vector of object, 10 hidden neurons, and 10 output unit. The first layer activation function is tansig and second layer activation function is purelin.The multipliers use 18 bits. The proposed design fits into the smallest Xilinx FPGAs3.Index Terms—feedforward backpropagation network, digit handwritten recognition, FPGA, Spartan-3
A Knowledge based segmentation algorithm for enhanced recognition of handwritten courtesy amounts
"March 1994."Includes bibliographical references (p. [23]-[24]).Supported by the Productivity From Information Technology (PROFIT) Research Initiative at MIT.Karim Hussein ... [et al.
Character Recognition Using A Modular Spatiotemporal Connectionist Model
We describe a connectionist model for recognizing handprinted characters. Instead of treating the input as a static signal, the image is scanned over time and converted into a time-varying signal. The temporalized image is processed by a spatiotemporal connectionist network suitable for dealing with time-varying signals. The resulting system offers several attractive features, including shift-invariance and inherent retention of local spatial relationships along the temporalized axis, a reduction in the number of free parameters, and the ability to process images of arbitrary length.
Connectionist networks were chosen as they offer learnability, rapid recognition, and attractive commercial possibilities. A modular and structured approach was taken in order to simplify network construction, optimization and analysis.
Results on the task of handprinted digit recognition are among the best report to date on a set of real-world ZIP code digit images, provided by the United States Postal Service. The system achieved a 99.1% recognition rate on the training set and a 96.0% recognition rate on the test set with no rejections. A 99.0% recognition rate on the test set was achieved when 14.6% of the images were rejected
The Implementation of Feedforward Backpropagation Algorithm for Digit Handwritten Recognition in a Xilinx Spartan-3
This research is aimed to implement feedforward backpropagation algorithm for digit handwritten recognition in an FPGA, Xilinx Spartan 3. This research is expected to give a contribution such as the feedforward algorithm design in VLSI technology based on FPGA, the practice module of Xilinx Spartan-3 development board and further research in artificial neural network and FPGA field in Electronics Laboratory.
The feedforward backpropagation algorithm is used to recognize 10 objects. The feedforward backpropagation network consists of two layers, 36 input unit which is the feature vector of object, 10 hidden neurons, and 10 output unit. The first layer activation function is tansig and second layer activation function is purelin.
The multipliers use 18 bits. The proposed design fits into the smallest Xilinx FPGAs3.
Index Terms—feedforward backpropagation network, digit handwritten recognition, FPGA, Spartan-3
Learning Robust Object Recognition Using Composed Scenes from Generative Models
Recurrent feedback connections in the mammalian visual system have been
hypothesized to play a role in synthesizing input in the theoretical framework
of analysis by synthesis. The comparison of internally synthesized
representation with that of the input provides a validation mechanism during
perceptual inference and learning. Inspired by these ideas, we proposed that
the synthesis machinery can compose new, unobserved images by imagination to
train the network itself so as to increase the robustness of the system in
novel scenarios. As a proof of concept, we investigated whether images composed
by imagination could help an object recognition system to deal with occlusion,
which is challenging for the current state-of-the-art deep convolutional neural
networks. We fine-tuned a network on images containing objects in various
occlusion scenarios, that are imagined or self-generated through a deep
generator network. Trained on imagined occluded scenarios under the object
persistence constraint, our network discovered more subtle and localized image
features that were neglected by the original network for object classification,
obtaining better separability of different object classes in the feature space.
This leads to significant improvement of object recognition under occlusion for
our network relative to the original network trained only on un-occluded
images. In addition to providing practical benefits in object recognition under
occlusion, this work demonstrates the use of self-generated composition of
visual scenes through the synthesis loop, combined with the object persistence
constraint, can provide opportunities for neural networks to discover new
relevant patterns in the data, and become more flexible in dealing with novel
situations.Comment: Accepted by 14th Conference on Computer and Robot Visio
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