디지털트랜스포메이션을 위한 의류 디자인 도면 이미지의 텍스트 검출 경량화 딥러닝 모델
A Lightweight Deep Learning Model for Text Detection in Fashion Design Sketch Images for Digital Transformation
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
본 논문에서는 의류 디자인 도면 이미지의 글자 검출을 위한 경량화된 딥러닝 네트워크를 제안하였다. 최근 의류 디자인 산업에서 Digital Transformation의 중요성이 대두되면서, 디지털 도구를활용한 의류 디자인 도면 작성이 강조되고 있으며, 디지털화된 의류 디자인 도면의 활용 가능성을 고려할 때, 도면에서 글자 검출과 인식이 중요한 첫 단계로 간주된다. 이 연구에서는 기존의글자 검출 딥러닝 모델을 기반으로 의류 도면 이미지의 특수성을 고려하여 경량화된 네트워크를설계하였으며, 별도로 수집한 의류 도면 데이터 셋을 추가하여 딥러닝 모델을 학습시켰다. 실험결과, 제안한 딥러닝 모델은 의류 도면 이미지에서 기존 글자 검출 모델보다 약 20% 높은 성능을보였다. 따라서 이 논문은 딥러닝 모델의 최적화와 특수한 글자 정보 검출 등의 연구를 통해 의류 디자인 분야에서의 Digital Transformation에 기여할 것으로 기대한다.
Abstract
In this paper, we propose a lightweight deep learning architecture tailored for efficient text detection in fashion design sketch images. Given the increasing prominence of Digital Transformation in the fashion industry, there is a growing emphasis on harnessing digital tools for creating fashion design sketches. As digitization becomes more pervasive in the fashion design process, the initial stages of text detection and recognition take on pivotal roles. In this study, a lightweight network was designed by building upon existing text detection deep learning models, taking into consideration the unique characteristics of apparel design drawings. Additionally, a separately collected dataset of apparel design drawings was added to train the deep learning model. Experimental results underscore the superior performance of our proposed deep learning model, outperforming existing text detection models by approximately 20% when applied to fashion design sketch images. As a result, this paper is expected to contribute to the Digital Transformation in the field of clothing design by means of research on optimizing deep learning models and detecting specialized text information.
참고문헌 (28)
- Dr.Bharati Rathore, "FashionTransformation 4.0: Beyond Digitalization & Marketing in Fashion Industry", Eduzone: International peer reviewed, 2021, pp.10(02), 54~59[DOI]
- O. Behr, "Fashion 4.0 – Digital Innovation in the Fashion Industry", Journal of technology and innovation management, 2(1), pp.1~9 (2018)
- 추호정, "IT 패션에 대한 국내 연구 동향", 한국의류산업학회지, 14(4), pp.614~628 (2012), 한국의류산업학회
- 노규성, "디지털 트랜스포메이션의 경쟁전략과의 적합성에 관한 연구", 한국융합학회논문지, 12(8), pp.257~265 (2021), 한국융합학회[DOI]
- B. Vial, "Understanding digital transformation: A review and a research agenda", Managing Digital Transformation, 54~ (2021)
- Chamorro-Premuzic, T., "The essential components of digital transformation", Harvard Business Review, 13, pp.1~6 (2021)
- Shaik Vaseem Akram, "Implementation of Digitalized Technologies for Fashion Industry 4.0: Opportunities and Challenges", Scientific Programming, 2022, pp.1~17 (2022), Hindawi Limited[DOI]
- 최란, "상하의 의류 영상을 이용한 가상 의류 착의 시스템", 멀티미디어학회논문지, 15(7), pp.941~950 (2012), 한국멀티미디어학회[DOI]
- Han Lin, "Review of Scene Text Detection and Recognition", Archives of Computational Methods in Engineering, 27(2), pp.433~454 (2020), Springer Science and Business Media LLC[DOI]
- M. Wang, "A novel scene text recognition method based on deep learning", Journal of Computers, Materials & Continua, 60(2), pp.781~794 (2019)
- J. Y. Baek, "Character Region Awareness for Text Detection", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.10507~10516 (2019)
- V. Shapiro, "Adaptive license plate image extraction", International Conference on Computer Systems and Technologies (2003)
- S. Kim, "A Robust License-plate Exeaction Method under Complex Image Conditions", the 16th Intemational Conference on Pattem Recognition (16th ICPR), pp.216~219 (2002)
- Han Li, "Probabilistic Hough Transform for Rectifying Industrial Nameplate Images: A Novel Strategy for Improved Text Detection and Precision in Difficult Environments", Applied Sciences, 13(7), pp.4533~ (2023), MDPI AG[DOI]
- M. Liao, "Textboxes++: A single-shot oriented scene text detector", Image Processing, 27(8), pp.3676~3690 (2018)
- "tesseract"[URL]
- "Jaided AI, EasyOCR"[URL]
- J. Baek, "What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis", International Conference on Computer Vision (2019)
- M. Jaderberg, "Spatial Transformer Networks", Proceedings of the 28th International Conference on Neural Information Processing Systems, pp.2017~2025 (2015)
- K. He, "Deep Residual Learning for Image Recognition", 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.770~778 (2016)
- B. Shi, "Robust Scene Text Recognition with Automatic Rectification", 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.4168~4176 (2016)
- A. Graves, "Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks", Proceedings of the 23rd international conference on Machine learningJune, pp.369~376 (2006)
- K. Simonyan, "Very deep convolutional networks for large-scale image recognition", Proceedings of the 27th International Conference on Neural Information Processing Systems, pp.1097~1105 (2014)
- "wkentaro, labelme"[URL]
- O. Ronneberger, "U-net: Convolutional networks for biomedical image segmentation", MICCAI 2015, pp.234~241 (2015)