Łukasiewicz Equivalent Neural Networks

Nola, Antonio Di; Lenzi, Giacomo; Vitale, Gaetano

doi:10.1007/978-3-319-33747-0_16

Cited by 8 publications

(1 citation statement)

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“…The reduced Feature set is fed to the MLP(Multi Layer Perceptron) Classifier and resultanalyzed. MLP is an algorithm that learns in a supervised manner [28]. It learns a function : → , ∈ 50, 100, 316, 733, 1523 , 14.The model consists of a 3 Layer Architecteure, Input Layer with neurons equals to the number of featuresand hidden layer with 100 neurons.Optimizer is Stochastic Gradient Descent .Values from the input layer are transformed by each neuron in the hidden layer with a weighted linear summation followed by ReLU activation function [30] defined as max 0, .…”

Section: A Neural Networkmentioning

confidence: 99%

Offline Handwritten Recognition of Malayalam District Name - A Holistic Approach

Jino¹,

Balakrishnan²

2017

IJET

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Major challenge for analytic recognition is the need for a proper segmentation algorithm. Even some times human beings may not be able to segment characters properly, in that case they will recognize it from the context or shape of the word. The proper selection of the feature purely depends on the language domain. In modern research we can find a lot of methods implementing machine extracted features rather than handcrafted features[6] [7]. Offline handwritten recognition has several applications like Address Interpretation, Writer Identification,Analyze the progress of a paralyzed patient and with a proper text to speech recognition system it can support visually challenged people. Lack of proper benchmarking database of offline handwritten images is a bottleneck for the researchers in Indian Languages. In Malayalam there is no benchmarking database available.The popular database available in the English language is CEDAR [8], MNIST[9], IAM[10] and CENPARAMI [11].The paper is organized as follows Section II reviews the literature, Section III explains the details about Malaylam Script and Datacollection method for the present work, Section IV describes the Proposed method, Section V discusses Results and Interpretation and Final Section is Conclusion.II. REVIEW OF LITERATURE In literature we can find several attempts using holistic approachfor the recognition of handwritten documents. This section discusses both online and offline handwriting recognition by applying holistic method. Online handwriting recognition of Bangla words achieved 97% accuracy for a lexicon size of 10.This approach uses four feature sets with multiple SVM and the final output is combined [12]. For Devanagary handwritten words Hidden Markov Model based approach with chain code features wasin used [13]. Lexicon size for the classification in this work is 100 and shows 80.

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Section: A Neural Networkmentioning

confidence: 99%