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Text-to-Speech device for patients with low vision
Arrahmah A.I.a, Rahmatika A.a, Harisa S.a, Zakaria H.a, Mengko R.a
a Department of Electrical Engineering, Bandung Institute of Technology, 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]© 2015 IEEE.With six meters highest visibility and 20 degrees maximum wide view, people who suffer from low vision are unable to see words and letters in ordinary newsprint. This fact makes the reading process becomes difficult that can disturb learning process and slow the patient’s intelligence development. Therefore, a device is needed to help them read easier. One of the device that are being developed today is a device that utilize another sense that is auditory sense. Text-to-Speech is a device that scans and reads Indonesian text book by changing it to voices. The purpose of the device is to process image as input into voice as output. This paper describes the design, implementation and experimental results of the device. This device consists of three modules, there are image processing module, words correction module and voice processing module. The device was developed based on Raspberry Pi v2 with 900 MHz processor speed. The audio output can be easily understood, it have less than 2% total error rate and processing time nearly two minutes for input text with A4 paper size. This device provides convenience for low vision people by leading them using voice, it also have the ability to play and stop the output while reading.[/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]Image Processing Module,Learning process,Low vision,Processing modules,Processing time,Processor speed,Text to speech,Total error rates[/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]image processing,low vision,text-to-speech,voice processing,word correction[/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/ICICI-BME.2015.7401365[/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]