A Normalization Process to Standardize Handwriting Data Collected from Multiple Resources for Recognition

Wang, Wen Li; Tang, Mei Huei

doi:10.1016/j.procs.2015.09.171

Cited by 7 publications

(4 citation statements)

References 11 publications

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“…Normalization as a preprocessing tool is utilized to make the method nearly invariant to the size, and position. Wang et al [59] propose to resize the image like zooming in to as the size normalization. For size normalization, Bilinear and Bi-cubic interpolation techniques are usually utilized in [24].…”

Section: Preprocessingmentioning

confidence: 99%

Two streams deep neural network for handwriting word recognition

Sulaiman

Omar

Nasrudin

2020

Multimed Tools Appl

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Handwritten word recognition is one of the hot topics in automatic handwritten text recognition that received a lot of attention in recent years. Unlike character recognition, word recognition deals with considerable variations in word shape and written style. This paper proposes a novel deep model for language-independent handwritten word recognition. The proposed deep structure has two parallel stages for jointly learning character and wordlevel information. In the character-level stage, a weakly character segmentation method is performed and then applies a series of Long short-term memory (LSTM) layers for character-level representation. The word-level stage employs a series of convolutional layers for the shape and structure representation of the word. These representations are then concatenated and followed by a series of fully connected layers for jointly learning the words and the character-level information. Since the character segmentation is language independent and error-prone, the proposed deep structure only applies weakly separation scheme and does not rely on any character segmentation algorithm. Thus, it effectively utilizes character level representation without bounding on any language model. In the proposed methodology, we use two new data augmentation strategies based on a psychological assumption to increase the model generalization performance. Experimental results on five public datasets including Arabic, English and German languages demonstrate that the proposed deep model has a superior performance to the state-of-the-art methods. Keywords Handwritten word recognition IntroductionDuring recent years, the handwriting word recognition(HWR) has become one of the most challenging and also, captivating research areas in the field of computer vision and pattern recognition. The HWR is used to describe the ability of the computers to translate a human

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Section: Preprocessingmentioning

confidence: 99%

Two streams deep neural network for handwriting word recognition

Sulaiman

Omar

Nasrudin

2020

Multimed Tools Appl

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“…In scientific calculation, it is necessary to prevent two very large numbers from calculating directly, which will lead to the loss of calculation precision and data information. Therefore, the normalization process is needed [Wang and Tang (2015)]. The minimum and maximum normalized normalization formula is used here.…”

Section: Gdm-sa-svr Yield Stress Modelmentioning

confidence: 99%

Yield Stress Prediction Model of RAFM Steel Based on the Improved GDM-SA-SVR Algorithm

Long¹,

He²

2019

Computers, Materials &Amp; Continua

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With the development of society and the exhaustion of fossil energy, researcher need to identify new alternative energy sources. Nuclear energy is a very good choice, but the key to the successful application of nuclear technology is determined primarily by the behavior of nuclear materials in reactors. Therefore, we studied the radiation performance of the fusion material reduced activation ferritic/martensitic (RAFM) steel. The main novelty of this paper are the statistical analysis of RAFM steel data sets through related statistical analysis and the formula derivation of the gradient descent method (GDM) which combines the gradient descent search strategy of the Convex Optimization Theory to get the best value. Use GDM algorithm to upgrade the annealing stabilization process of simulated annealing algorithm. The yield stress performance of RAFM steel is successfully predicted by the hybrid model which is combined by simulated annealing (SA) with support vector machine (SVM) as the first time. The effect on yield stress by the main physical quantities such as irradiation temperature, irradiation dose and test temperature is also analyzed. The related prediction process is: first, we used the improved annealing algorithm to optimize the SVR model after training the SVR model on a training data set. Next, we established the yield stress prediction model of RAFM steel. The model can predict up to 96% of the data points with the prediction in the test set and the original data point in the 2σ range. The statistical test analysis shows that under the condition of confidence level α=0.01, the calculation results of the regression effect significance analysis pass the T-test.

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“…Geometrical growth in computational power has enabled the implementation of the current character recognition methodologies [2], [9]- [12]. This also creates an increasing demand on many emerging application domains of character recognition which require more advanced methodologies [13]- [17]. The process of optical character recognition (OCR) includes scanning of the text character-by-character, analysis of scanned images, and the translation of the character image into character codes, commonly used in data processing [6], [14].…”

Section: Introductionmentioning

confidence: 99%

“…In the offline technique, the system recognizes the fixed static shape of the character [3], while online character recognition instead recognizes the dynamic motion during handwriting. In offline handwriting recognition, information captured are available on paper, which is digitized through the use of scanner [17]- [21].…”

Section: Introductionmentioning

confidence: 99%

Yoruba Handwritten Character Recognition using Freeman Chain Code and K-Nearest Neighbor Classifier

Ajao

Olawuyi

Odejobi

2018

Jurnal Teknologi dan Sistem Komputer

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This work presents a recognition system for Offline Yoruba characters recognition using Freeman chain code and K-Nearest Neighbor (KNN). Most of the Latin word recognition and character recognition have used k-nearest neighbor classifier and other classification algorithms. Research tends to explore the same recognition capability on Yoruba characters recognition. Data were collected from adult indigenous writers and the scanned images were subjected to some level of preprocessing to enhance the quality of the digitized images. Freeman chain code was used to extract the features of THE digitized images and KNN was used to classify the characters based on feature space. The performance of the KNN was compared with other classification algorithms that used Support Vector Machine (SVM) and Bayes classifier for recognition of Yoruba characters. It was observed that the recognition accuracy of the KNN classification algorithm and the Freeman chain code is 87.7%, which outperformed other classifiers used on Yoruba characters.

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A Normalization Process to Standardize Handwriting Data Collected from Multiple Resources for Recognition

Cited by 7 publications

References 11 publications

Two streams deep neural network for handwriting word recognition

Two streams deep neural network for handwriting word recognition

Yield Stress Prediction Model of RAFM Steel Based on the Improved GDM-SA-SVR Algorithm

Yoruba Handwritten Character Recognition using Freeman Chain Code and K-Nearest Neighbor Classifier

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