Deep Learning Method for Handwriting Recognition
The advancement of technology nowadays resulted into documents, such as forms and petitions, being filled out in computer and digital environment. Yet in some cases, documents are still preserved in traditional style, on print. Due to its distinct proportions, however, its storage, sharing and filin...
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| Format: | Article |
| Language: | English |
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Kyrgyz Turkish Manas University
2021-06-01
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| Series: | MANAS: Journal of Engineering |
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| Online Access: | https://dergipark.org.tr/en/download/article-file/1483812 |
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| author | Abdullah Erdal Tümer Ayşe Ayvacı Erdoğan |
| author_facet | Abdullah Erdal Tümer Ayşe Ayvacı Erdoğan |
| author_sort | Abdullah Erdal Tümer |
| collection | DOAJ |
| description | The advancement of technology nowadays resulted into documents, such as forms and petitions, being filled out in computer and digital environment. Yet in some cases, documents are still preserved in traditional style, on print. Due to its distinct proportions, however, its storage, sharing and filing has become a complication. The relocation of these written documents to digital environment is therefore of great significance. In this view, this study aims to explore methodologies of digitizing handwritten documents. In this study, the documents converted to image format were pre-processed using image processing methods. These operations include dividing lines of the document into image format, dividing into words which then divided into characters, and finally, a classification operation on the characters. As classification phase, one of the deep learning methods is the Convolution Neural Network method is used in image recognition. The model was trained using the EMNIST dataset, and in the character, dataset created from the documents at hand. The dataset created had a success rate of 87.81%. Characters classified as finishers are sequentially combined and the document is transferred to the computer afterwards. |
| format | Article |
| id | doaj-art-90953ecc47fa4d60b918039aa00f3c4f |
| institution | Kabale University |
| issn | 1694-7398 |
| language | English |
| publishDate | 2021-06-01 |
| publisher | Kyrgyz Turkish Manas University |
| record_format | Article |
| series | MANAS: Journal of Engineering |
| spelling | doaj-art-90953ecc47fa4d60b918039aa00f3c4f2025-02-03T12:07:27ZengKyrgyz Turkish Manas UniversityMANAS: Journal of Engineering1694-73982021-06-0191859210.51354/mjen.8523121437Deep Learning Method for Handwriting RecognitionAbdullah Erdal Tümer0https://orcid.org/0000-0001-7747-9441Ayşe Ayvacı Erdoğan1https://orcid.org/0000-0003-4466-4557KIRGIZİSTAN-TÜRKİYE MANAS ÜNİVERSİTESİNECMETTİN ERBAKAN ÜNİVERSİTESİ, FEN BİLİMLERİ ENSTİTÜSÜThe advancement of technology nowadays resulted into documents, such as forms and petitions, being filled out in computer and digital environment. Yet in some cases, documents are still preserved in traditional style, on print. Due to its distinct proportions, however, its storage, sharing and filing has become a complication. The relocation of these written documents to digital environment is therefore of great significance. In this view, this study aims to explore methodologies of digitizing handwritten documents. In this study, the documents converted to image format were pre-processed using image processing methods. These operations include dividing lines of the document into image format, dividing into words which then divided into characters, and finally, a classification operation on the characters. As classification phase, one of the deep learning methods is the Convolution Neural Network method is used in image recognition. The model was trained using the EMNIST dataset, and in the character, dataset created from the documents at hand. The dataset created had a success rate of 87.81%. Characters classified as finishers are sequentially combined and the document is transferred to the computer afterwards.https://dergipark.org.tr/en/download/article-file/1483812character recognitionconvolutional neural networkdeep learninghandwriting recognitionimage processing |
| spellingShingle | Abdullah Erdal Tümer Ayşe Ayvacı Erdoğan Deep Learning Method for Handwriting Recognition MANAS: Journal of Engineering character recognition convolutional neural network deep learning handwriting recognition image processing |
| title | Deep Learning Method for Handwriting Recognition |
| title_full | Deep Learning Method for Handwriting Recognition |
| title_fullStr | Deep Learning Method for Handwriting Recognition |
| title_full_unstemmed | Deep Learning Method for Handwriting Recognition |
| title_short | Deep Learning Method for Handwriting Recognition |
| title_sort | deep learning method for handwriting recognition |
| topic | character recognition convolutional neural network deep learning handwriting recognition image processing |
| url | https://dergipark.org.tr/en/download/article-file/1483812 |
| work_keys_str_mv | AT abdullaherdaltumer deeplearningmethodforhandwritingrecognition AT ayseayvacıerdogan deeplearningmethodforhandwritingrecognition |