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Mobile Client-Server Approach for Handwriting Digit Recognition
Shiddieqy H.A.a, Adiono T.a, Syafalni I.a
a School of Electrical Engineering and Informatics, Institut Teknologi Bandung, University Center of Excellence on Microelectronic, Institut Teknologi Bandung, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2019 IEEE.In the era of an Internet of Things, pattern recognition technology is growing rapidly, especially by the massive implementations of artificial intelligence (AI). Selecting the right implementation for an AI algorithm for an application could be quite challenging and time-consuming. In this paper, we propose a client-server system implementation for handwriting digit recognition. A client-server is set based on TensorFlow with multiple models for classifications. The client is set based on a mobile application that the user inputs the digit by touch panel of the mobile. In this paper, the models at the server are trained and tested by using MNIST database of handwritten. In addition, we use convolutional neural network (CNN) to improve the performance of the neural network. The client-server allows many users to be accessed by AI model from the same time. The advantage of using client-server approach is reducing power and time for processing handwriting recognition in client device. It also speeds up the time of development and implementation of algorithm on the server.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Client devices,Client-server systems,Convolutional neural network,Digit recognition,Handwriting digit recognition,Handwriting recognition,Mobile applications,Pattern recognition technologies[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]artificial intelligence,Client-server system,digit recognition,mobile application[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ISESD.2019.8909448[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]