| Autor: |
Wahyu Andi Saputra, Muhammad Zidny Naf’an, Asyhar Nurrochman |
| Jazyk: |
angličtina |
| Rok vydání: |
2019 |
| Předmět: |
|
| Zdroj: |
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), Vol 3, Iss 3, Pp 524-531 (2019) |
| Druh dokumentu: |
article |
| ISSN: |
2580-0760 |
| DOI: |
10.29207/resti.v3i3.1338 |
| Popis: |
Form sheet is an instrument to collect someone’s information and in most cases it is used in a registration or submission process. The challenge being faced by physical form sheet (e.g. paper) is how to convert its content into digital form. As a part of study of computer vision, Optical Character Recognition (OCR) recently utilized to identify hand-written character by learning pattern characteristics of an object. In this research, OCR is implemented to facilitate the conversion of paper-based form sheet's content to be stored properly into digital storage. In order to recognize the character's pattern, this research develops training and testing method in a Convolutional Neural Network (CNN) environment. There are 262.924 images of hand-written character sample and 29 paper-based form sheets from SDN 01 Gumilir Cilacap that implemented in this research. The form sheets also contain various sample of human-based hand-written character. From the early experiment, this research results 92% of accuracy and 23% of loss. However, as the model is implemented to the real form sheets, it obtains average accuracy value of 63%. It is caused by several factors that related to character's morphological feature. From the conducted research, it is expected that conversion of hand-written form sheets become effortless. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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