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Printed Arabic letter recognition based on image
Radhiah A.a, Machbub C.a, Hidayat E.M.I.a, Prihatmanto A.S.a
a School of Electrical Engineering and Informatics, 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]© 2018 IEEE.In the process of learning Arabic letters, recognizing Arabic letters is a very important part. Learning Arabic letters will be more effective by using a system that can recognize Arabic letters, both in isolated and sentence form. Most of the developed techniques for classifying characters in other languages cannot be used for Arabic characters due to the differences in the structure. Arabic letters are cursive in general. In this research, we proposed an Arabic letter recognition system that can recognize both forms. In the classification stage, we compared Neural Network and Hidden Markov Model. The result of the recognition of isolated Arabic letters using the ANN classification method for isolated letters reaches 100% accuracy and the result of letter recognition in the sentence reaches 69% accuracy. While the result of the recognition of Arabic letters using HMM for isolated letters reaches 71% accuracy and the result of letter recognition in the sentence reaches 50% accuracy. From the data obtained on the Arabic letters incorrectly identified with the ANN method, there are 5 letters that are always misidentified. The accuracy of the letters correctly identified is 50%, whereas from the data obtained on the Arabic letters incorrectly identified with the HMM method, there are 9 letters that are always misidentified, The accuracy of the letters correctly identified is 38%[/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]ANN classification,Arabic characters,Chain codes,Letter recognition,Markov model,Process of learning[/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]Arabic Letter Recognition,Chain code,Hiden Markov Model,Neural Network,Stentiford Algorithm[/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/ICSIGSYS.2018.8373574[/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]