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A Time Delay Neural Network for Online Arabic Handwriting Recognition [chapter]

Ramzi Zouari, Houcine Boubaker, Monji Kherallah
2017 Advances in Intelligent Systems and Computing  
These strokes are used to train the Time Delay Neural Network (TDNN) which is able to represent the sequential aspect of input data.  ...  Handwriting recognition is an interesting part in pattern recognition field.  ...  Time Delay Neural Network The TDNN, Time Delay Neural Network is a convolution neural network (CNN) which was first introduced for speech recognition [19] .  ... 
doi:10.1007/978-3-319-53480-0_99 fatcat:telovoadcrdhrkjdrxiz2zqfrq

Multi-feature Learning by Joint Training for Handwritten Formula Symbol Recognition

Dingbang Fang, Chenhao Zhang
2020 IEEE Access  
Given the similarity of handwritten formula symbols and various handwriting styles, this paper proposes a squeeze-extracted multi-feature convolution neural network (SE-MCNN) to improve the recognition  ...  The standard mathematical formula symbol library provided by the Competition Organization on Recognition of Online Handwritten Mathematical Expression (CROHME) is used to verify the effectiveness of the  ...  CONCLUSION This paper proposes a novel convolutional neural network, SE-MCNN, to improve the accuracy of handwritten formula symbol recognition.  ... 
doi:10.1109/access.2020.2979346 fatcat:2d7hailr6bd6tfuofq6xg45cpm


P Thangamariappan, Dr.J.C Miraclin Joyce Pamila
2020 International Journal of Engineering Applied Sciences and Technology  
This paper performs the analysis of accuracies and performance measures of algorithm Convolutional Neural Networks (CNN). The proposed approach recognizes with overall accuracy is 93%.  ...  This paper presents the result of handwritten recognition using deep learning. Handwriting is unique to each individual. So the handwriting is differed from one person to another person.  ...  CREATE THE MODEL Here using a simple Multi-Layer Perceptron (MLP) as our neural network model. Also the neural network model is used in 784 input neurons. Two hidden layers are used.  ... 
doi:10.33564/ijeast.2020.v04i11.099 fatcat:ye4ojkwthjdcncyke7izgi72zq

Handwriting to Text Conversion for English Language Using Deep Learning

Ketaki G. Dhotre, Harshali K. Ghumate, Mayuri Mane, Prof. Savita Lade
2022 International Journal for Research in Applied Science and Engineering Technology  
To conduct horizontalvertical segmentation, we used OpenCV. Keywords: Handwriting, Bi-LSTM, Convolutional Neural Network, Text Conversion, Deep Learning, OpenCV  ...  Handwriting recognition is one of the most active study areas, and deep neural networks are being used in it. Humans find it simple to recognise handwriting, but computers find it tough.  ...  CNN (Convolution Neural Networks) In this section, we study the usage of convolutional neural networks (CNNs) in the context of handwritten recognition.  ... 
doi:10.22214/ijraset.2022.40876 fatcat:vkov3mhm4fcf5a467idhuwsp7y

Table of Contents

2018 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR)  
Handwritten Chinese Character Recognition using Two-Stage Convolutional Neural Bangalore) Network Multi-Perspective Multi-Modal Trajectory Descriptions for Handwritten Strokes Zhe Li (South China University  ...  Oral Session 5: Recurrent Neural Networks for Character Recognition Fully Convolutional Networks for Handwriting Recognition Felipe Petroski Such (Rochester Institute of Technology), Dheeraj Peri (Rochester  ... 
doi:10.1109/icfhr-2018.2018.00004 fatcat:u3d6xtmfsrawhdetd7tkoq2eue


D.J. Samatha Naidu, T. Mahammad Rafi
2021 International journal of computer science and mobile computing  
The convolutional neural network is the current state of neural network which has wide application in fields like image, video recognition.  ...  To Recognize the tamil characters they have use neural networks with the Kohonen self-organizing map(SOM) which is an unsupervised neural networks.  ...  INTRODUCTION Handwriting Recognition is one of the areas where mostly researcher uses deep neural networks.  ... 
doi:10.47760/ijcsmc.2021.v10i08.007 fatcat:wmzk77srybfcjdoa5btkkvtwii

The Study of Handwriting Recognition Algorithms Based on Neural Networks

Barak Finkelstein, Athabasca University , Canada, Kaplan Kuncan, Athabasca University , Canada
2021 International Journal of Hybrid Information Technology  
Therefore, the study of hand-written word recognition should be implemented using CNN through the network. Handwriting Word Recognition is the key technique for self-identification.  ...  Therefore, summarizing and analyzing the existing handwritten digit recognition algorithms, two handwritten digit recognition algorithms based on Convolutional Neural Network (CNN) are proposed.  ...  Neural networks include feed-forward neural networks [5] , BP neural networks [6] , deep belief networks, and convolutional neural networks [7] .  ... 
doi:10.21742/ijhit.2021.14.1.05 fatcat:cdsakc65bngsxazidbbwkjqyai

Indic Handwritten Script Identification using Offline-Online Multimodal Deep Network [article]

Ayan Kumar Bhunia, Subham Mukherjee, Aneeshan Sain, Ankan Kumar Bhunia, Partha Pratim Roy, Umapada Pal
2019 arXiv   pre-print
Thereafter, we feed this offline-online modality pair to our network.  ...  Our method uses a multimodal deep network which takes both offline and online modality of the data as input in order to explore the information from both the modalities jointly for script identification  ...  One of the most promising future research directions would be to design a single deep model for offline and online handwriting recognition in a single deep neural network through exploring information  ... 
arXiv:1802.08568v3 fatcat:tycp3xjjfjcsncyorib42l3vxm

Vietnamese handwritten character recognition using convolutional neural network

Truong Quang Vinh, Le Hoai Duy, Nguyen Thanh Nhan
2020 IAES International Journal of Artificial Intelligence (IJ-AI)  
This paper presents an efficient model for Vietnamese handwriting character recognition by Convolutional Neural Networks (CNNs) – a kind of deep neural network model can achieve high performance on hard  ...  The proposed architecture of the CNN network for Vietnamese handwriting character recognition consists of five hidden layers in which the first 3 layers are convolutional layers and the last 2 layers are  ...  The Feature Classification takes the output of the Feature Extraction Vietnamese handwritten character recognition using convolutional neural network (Truong Quang Vinh) 58,000 examples in training set  ... 
doi:10.11591/ijai.v9.i2.pp276-281 fatcat:qsuekm5645glzpm3qf6ael5vaa

Various Models for the Conversion of Handwritten Text to Digital Text

Bhavyasri Maddineni
2021 International Journal for Research in Applied Science and Engineering Technology  
, convolution neural networks, and recurrent neural networks.  ...  So, HTR/HWR has an increasing use these days. There are various techniques used in recognizing the handwriting.  ...  Handwritten Chinese Text Recognition using Separable Multi -Dimensional Recurrent Neural Network In this paper, the recognition model is designed using Separable MDLSTM (SMDLSTM).  ... 
doi:10.22214/ijraset.2021.35616 fatcat:7y4374xh4bfvvkoj75udqw4x3y

Open Source Dataset and Deep Learning Models for Online Digit Gesture Recognition on Touchscreens [article]

Philip J. Corr, Guenole C. Silvestre, Chris J. Bleakley
2017 arXiv   pre-print
published models for offline handwriting recognition from scanned images.  ...  This paper presents an evaluation of deep neural networks for recognition of digits entered by users on a smartphone touchscreen.  ...  To date, there has been almost no work on using neural networks for online recognition of touchscreen handwriting using a finger or thumb.  ... 
arXiv:1709.06871v1 fatcat:g36wop3ly5aprpptngwycpa474

Handwriting Recognition Based on 3D Accelerometer Data by Deep Learning

Pedro Lopez-Rodriguez, Juan Gabriel Avina-Cervantes, Jose Luis Contreras-Hernandez, Rodrigo Correa, Jose Ruiz-Pinales
2022 Applied Sciences  
This paper proposes a handwritten character recognition system based on 3D accelerometer signal processing using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM).  ...  Online handwriting recognition has been the subject of research for many years. Despite that, a limited number of practical applications are currently available.  ...  [17] presented a solution to handwriting recognition by developing a user interface to compute numeral recognition in air writing by using a Convolutional Neural Network (CNN).  ... 
doi:10.3390/app12136707 fatcat:i6kxikpvxjbq7geaq5zneiazry

Sentence-level Online Handwritten Chinese Character Recognition [article]

Yunxin Li, Qian Yang, Qingcai Chen, Lin Ma, Baotian Hu, Xiaolong Wang, Yuxin Ding
2021 arXiv   pre-print
It couples convolutional neural network with sequence modeling architecture to exploit the handwritten character's previous contextual information.  ...  Single online handwritten Chinese character recognition~(single OLHCCR) has achieved prominent performance.  ...  Vanilla Compositional Network For sentence-level OLHCCR, an intuitive approach is to use deep convolutional neural network for single handwritten Chinese character representation with one followed language  ... 
arXiv:2108.02561v1 fatcat:pocccqgg6rf7to7ddwcijiibnu

Review of offline handwritten text recognition in south Indian languages

A. T. Anju, Binu P. Chacko, K. P. Mohammad Basheer
2021 Malaya Journal of Matematik  
Convolutional Neural Networks (CNNs) and classifier combination methods have provided better performance among proposals provided by the researchers.  ...  A description about south Indian languages and an overview of general handwriting recognition systems are also presented briefly.  ...  described a method for handwriting recognition using Modified Convolutional Neural Network (M-CNN).  ... 
doi:10.26637/mjm0901/0132 fatcat:6ckkcuiakbcivljp45occntya4

Online handwritten Gurmukhi word recognition using fine-tuned Deep Convolutional Neural Network on offline features

Sukhdeep Singh, Anuj Sharma, Vinod Kumar Chauhan
2021 Machine Learning with Applications  
The present study provided benchmark results for online handwritten Gurmukhi word recognition using deep learning architecture convolutional neural network, and obtained above 97% recognition accuracy  ...  Moreover, the proposed architecture can be used to improve the benchmark results of online handwriting recognition of several major Indian scripts.  ...  Convolutional Neural Network Amongst different DL models, the CNN is the most widely used, especially in image recognition. The CNN is a particular type of multi-layer NN.  ... 
doi:10.1016/j.mlwa.2021.100037 fatcat:ohu2dktiifft3lyecf6g3yuauu
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