Realization of character recognition application on text images by convolutional neural network
Küçük Resim Yok
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Gazi Univ, Fac Engineering Architecture
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
In the literature, effective and common technology used for in door navigation has not been found. For the solution of the problem, recognition of the text images used indoors and determination of the current location according to the images seems to be the most feasible solution. In this study, deep learning methods, which are used successfully in many fields, were used to recognize signage images used in interior spaces. The created model extracts high-level features such as corners and edges in the images with the Convolutional Neural Network. Next, the sequencing properties of the letters are preserved with the Recurrent Neural Network-Based Bidirectional Long-Short-Term Memory. The Connectionist Temporal Classification approach is used to solve the repetitions and other problems in the sequence. Mistakes in words created with the characters generated by the trained model are improved using Levenshtein distance. The success of the model in the tests performed with the Synth90k data set was 96%, and the success in Turkish images with the same character set of the model trained with the Sytnh90kwas 93%. The results obtained demonstrate the success of the proposed approach.
Açıklama
Anahtar Kelimeler
Character recognition, convolutional recurrent neural network, deep learning, levenshtein distance, recurrent neural network
Kaynak
Journal of the Faculty of Engineering and Architecture of Gazi University
WoS Q Değeri
Q4
Scopus Q Değeri
Q2
Cilt
37
Sayı
1
