scholarly journals On the Performance Improvement of Devanagari Handwritten Character Recognition

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Pratibha Singh ◽  
Ajay Verma ◽  
Narendra S. Chaudhari
Keyword(s):  
Performance Improvement ◽  
Character Recognition ◽  
Gradient Descent ◽  
Stochastic Gradient Descent ◽  
Weight Decay ◽  
Pixel Intensity ◽  
Handwritten Character ◽  
Extraction Algorithm ◽  
Numeral Recognition ◽  
Gradient Feature

The paper is about the application of mini minibatch stochastic gradient descent (SGD) based learning applied to Multilayer Perceptron in the domain of isolated Devanagari handwritten character/numeral recognition. This technique reduces the variance in the estimate of the gradient and often makes better use of the hierarchical memory organization in modern computers.L2-weight decay is added on minibatch SGD to avoid overfitting. The experiments are conducted firstly on the direct pixel intensity values as features. After that, the experiments are performed on the proposed flexible zone based gradient feature extraction algorithm. The results are promising on most of the standard dataset of Devanagari characters/numerals.

scholarly journals Optical Recognition of Handwritten Logic Formulas Using Neural Networks

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2761
Author(s):  
Vaios Ampelakiotis ◽  
Isidoros Perikos ◽  
Ioannis Hatzilygeroudis ◽  
George Tsihrintzis
Keyword(s):  
Neural Networks ◽  
Character Recognition ◽  
Gradient Descent ◽  
Feedforward Neural Networks ◽  
Stochastic Gradient ◽  
Stochastic Gradient Descent ◽  
Training Algorithms ◽  
Gradient Descent Algorithm ◽  
Two Stages ◽  
And Training

In this paper, we present a handwritten character recognition (HCR) system that aims to recognize first-order logic handwritten formulas and create editable text files of the recognized formulas. Dense feedforward neural networks (NNs) are utilized, and their performance is examined under various training conditions and methods. More specifically, after three training algorithms (backpropagation, resilient propagation and stochastic gradient descent) had been tested, we created and trained an NN with the stochastic gradient descent algorithm, optimized by the Adam update rule, which was proved to be the best, using a trainset of 16,750 handwritten image samples of 28 × 28 each and a testset of 7947 samples. The final accuracy achieved is 90.13%. The general methodology followed consists of two stages: the image processing and the NN design and training. Finally, an application has been created that implements the methodology and automatically recognizes handwritten logic formulas. An interesting feature of the application is that it allows for creating new, user-oriented training sets and parameter settings, and thus new NN models.

scholarly journals Multiclass Recognition of Offline Handwritten Devanagari Characters using CNN

2020 ◽  
Vol 5 (6) ◽  
pp. 1429-1439
Author(s):  
Mamta Bisht ◽  
Richa Gupta
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Character Recognition ◽  
Gradient Descent ◽  
Recognition Task ◽  
Stochastic Gradient ◽  
Stochastic Gradient Descent ◽  
Handwritten Character Recognition ◽  
Handwritten Documents ◽  
Acceptable Accuracy

The handwriting style of every writer consists of variations, skewness and slanting nature and therefore, it is a stimulating task to recognise these handwritten documents. This article presents a study on various methods available in literature for Devanagari handwritten character recognition and performs its implementation using Convolutional neural network (CNN). Available methods are studied on different parameters and a tabular comparison is also presented which concludes superiority of CNN model in character recognition task. The proposed CNN model results in well acceptable accuracy using dropout and stochastic gradient descent (SGD) optimizer.

scholarly journals Convolutional ensembles for Arabic Handwritten Character and Digit Recognition

2018 ◽  
Vol 4 ◽  
pp. e167 ◽  
Author(s):  
Iam Palatnik de Sousa
Keyword(s):  
Gradient Descent ◽  
Cross Validation ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Parameter Tuning ◽  
Stochastic Gradient Descent ◽  
Digit Recognition ◽  
Handwritten Character ◽  
Monte Carlo Cross Validation ◽  
Fold Cross Validation

A learning algorithm is proposed for the task of Arabic Handwritten Character and Digit recognition. The architecture consists on an ensemble of different Convolutional Neural Networks. The proposed training algorithm uses a combination of adaptive gradient descent on the first epochs and regular stochastic gradient descent in the last epochs, to facilitate convergence. Different validation strategies are tested, namely Monte Carlo Cross-Validation and K-fold Cross Validation. Hyper-parameter tuning was done by using the MADbase digits dataset. State of the art validation and testing classification accuracies were achieved, with average values of 99.74% and 99.47% respectively. The same algorithm was then trained and tested with the AHCD character dataset, also yielding state of the art validation and testing classification accuracies: 98.60% and 98.42% respectively.

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