Recognition of Eye-Written Characters Using Deep Neural Network

Chang, Won-Du; Choi, Jae-Hyeok; Shin, Jungpil

doi:10.3390/app112211036

Cited by 3 publications

(6 citation statements)

References 21 publications

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“…Chang et. al [10], employed a DNN with 4 parallel convolutional layers having various kernel sizes and filters to extract the local features from the patterns of Arabic numbers. The outcomes were ensemble by training the DNN 10 times and obtaining the median score.…”

Section: Related Workmentioning

confidence: 99%

“…To solve the non-uniformity and enhance the recognition performance of eye-writing, authors in [10] utilized DNN with an inception module for the recognition of strokebased Arabic numerals. The network was ensembled by training the network ten times with various weights and obtaining the median of the outputs.…”

Section: Problem Analysismentioning

confidence: 99%

“…However, the DNN network could not distinguish patterns with long-term eye fixation and nonuniformly written characters. Thus, the method in [10] cannot be generalized to other complex eye-writing such as English letters. Figure 1 shows the patterns of some eyewriting characters that are non-uniformly written by two different users in two attempts.…”

Section: Problem Analysismentioning

confidence: 99%

“…Chang et. al [10] minimized a few pre-processing steps and used the inception architecture of deep neural network (DNN) to extract the input patterns variation of the Arabic numerals. Due to fluctuation in the training results, the DNN was ensembled and trained ten times with various weights to obtain the median score.…”

Section: Introductionmentioning

confidence: 99%

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Translated Pattern-Based Eye-Writing Recognition Using Dilated Causal Convolution Network

Bature,

Abdullahi,

Chiracharit

et al. 2024

IEEE Access

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Recently, eye-writing has been used as a novel language communication method, in which the paths of eye movement are detected for character recognition. However, instability of the eyes causes gaze points to form a character with a non-uniform shape and distinct writing style across participants. The non-uniformity affected the performance of the recognition algorithms and set back the applicability of eyewriting. In this paper root translation and dilated causal convolutional (DCC) layers are utilized to model the non-uniformity in eye-writing patterns. The root translation shifted the pattern to have uniform root gaze points by obtaining the difference between the initial gaze points and subsequent gaze points. The translated patterns were used to train the temporal convolution network (TCN) having three stacked DCC layers with different filter and dilation factors. The DCC layers extract temporal dependencies in the pattern by convolving a particular gaze point with a certain previous gaze point within its receptive field. To evaluate the performance of the proposed method, a dataset of 36 eye-writing characters including 26 English and 10 Arabic numerals was recorded from 20 participants using Tobii eye tracker. The evaluation results depicted that our proposed method achieved an accuracy of 96.20% on our newly designed English and Arabic numeral datasets. The proposed method outperform conventional methods by achieving 98.81%, 97.76%, and 93.51% on HideMyGaze, Complex gaze gesture, and Isolated Japanese Katakana datasets, respectively.

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Section: Related Workmentioning

confidence: 99%

Section: Problem Analysismentioning

confidence: 99%

Section: Problem Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Translated Pattern-Based Eye-Writing Recognition Using Dilated Causal Convolution Network

Bature,

Abdullahi,

Chiracharit

et al. 2024

IEEE Access

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show abstract

“…Ding et al applied also DTW for character recognition [37]. Chang et al proposed to use an ensemble deep neural network (DNN) with inception modules for eye-writing [38]. Kang et al applied an ensemble network with attention mechanisms [39].…”

Section: Introductionmentioning

confidence: 99%

eyeSay: Brain Visual Dynamics Decoding With Deep Learning & Edge Computing

Zou

Zhang

2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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Brain visual dynamics encode rich functional and biological patterns of the neural system, and if decoded, are of great promise for many applications such as intention understanding, cognitive load quantization and neural disorder measurement. We here focus on the understanding of the brain visual dynamics for the Amyotrophic lateral sclerosis (ALS) population, and propose a novel system that allows these so-called 'lock-in' patients to 'speak' with their brain visual movements. More specifically, we propose an intelligent system to decode the eye bio-potential signal, Electrooculogram (EOG), thereby understanding the patients' intention. We first propose to leverage a deep learning framework for automatic feature learning and classification of the brain visual dynamics, aiming to translate the EOG to meaningful words. We afterwards design and develop an edge computing platform on the smart phone, which can execute the deep learning algorithm, visualize the brain visual dynamics, and demonstrate the edge inference results, all in real-time. Evaluated on 4,500 trials of brain visual movements performed by multiple users, our novel system has demonstrated a high eyeword recognition rate up to 90.47%. The system is demonstrated to be intelligent, effective and convenient for decoding brain visual dynamics for ALS patients. This research thus is expected to greatly advance the decoding and understanding of brain visual dynamics, by leveraging machine learning and edge computing innovations.

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Recognition of Eye-Written Characters with Limited Number of Training Data Based on a Siamese Network

Kang

Chang

2021

Electronics

Self Cite

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Developing a hum–computer interface (HCI) is essential, especially for those that have spinal cord injuries or paralysis, because of the difficulties associated with the application of conventional devices and systems. Eye-writing is an HCI that uses eye movements for writing characters such that the gaze movements form letters. In addition, it is a promising HCI because it can be utilized even when voices and hands are inaccessible. However, eye-writing HCI has low accuracy and encounters difficulties in obtaining data. This study proposes a method for recognizing eye-written characters accurately and with limited data. The proposed method is constructed using a Siamese network, an attention mechanism, and an ensemble algorithm. In the experiment, the proposed method successfully classified the eye-written characters (Arabic numbers) with high accuracy (92.78%) when the ratio of training to test data was 2:1. In addition, the method was tested as the ratio changed, and 80.80% accuracy was achieved when the number of training data was solely one-tenth of the test data.

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Recognition of Eye-Written Characters Using Deep Neural Network

Cited by 3 publications

References 21 publications

Translated Pattern-Based Eye-Writing Recognition Using Dilated Causal Convolution Network

Translated Pattern-Based Eye-Writing Recognition Using Dilated Causal Convolution Network

eyeSay: Brain Visual Dynamics Decoding With Deep Learning & Edge Computing

Recognition of Eye-Written Characters with Limited Number of Training Data Based on a Siamese Network

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