| Yazarlar (3) |
Doğukan Uykun
Ankara Üniversitesi, Türkiye |
|
Hakkı Alparslan Ilgın
Ankara Üniversitesi, Türkiye |
Dr. Öğr. Üyesi Memduh KÖSE
Kırşehir Ahi Evran Üniversitesi, Türkiye |
| Özet |
| Detection and recognition of text in videos present significant challenges due to the wide range of font styles, varying text sizes, and diverse lighting conditions that can affect readability. The ability to accurately and efficiently detect text in such dynamic environments is essential for extracting meaningful information and enabling further processing. In this paper, real-time text detection and recognition for Turkish language has been analysed using different approaches. To identify the most suitable approach, multiple models, including EasyOCR, Tesseract, You Only Look Once, and Discrete Cosine Transform in conjunction with support vector machines were evaluated under different conditions. Since Turkish alphabet has similar letters, comparisons aim to improve the accuracy and speed of text extraction from video content, choosing a practical solution for real-time applications where precise text recognition is crucial. |
| Anahtar Kelimeler |
| Convolutional Neural Networks | Deep Learning | Image Processing | Machine Learning | Optical Character Recognition |
| Bildiri Türü | Tebliğ/Bildiri |
| Bildiri Alt Türü | Tam Metin Olarak Yayınlanan Tebliğ (Uluslararası Kongre/Sempozyum) |
| Bildiri Niteliği | Alanında Hakemli Uluslararası Kongre/Sempozyum |
| Doi Numarası | 10.1109/ISAS66241.2025.11101765 |
| Bildiri Dili | İngilizce |
| Kongre Adı | 9th International Symposium on Innovative Approaches in Smart Technologies (ISAS) |
| Kongre Tarihi | 27-06-2025 / |
| Basıldığı Ülke | Türkiye |
| Basıldığı Şehir | Gaziantep |
| Atıf Sayıları |
