{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T15:49:26Z","timestamp":1763567366884,"version":"3.45.0"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T00:00:00Z","timestamp":1762214400000},"content-version":"vor","delay-in-days":3,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J. King Saud Univ. Comput. Inf. Sci."],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1007\/s44443-025-00327-5","type":"journal-article","created":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T13:50:48Z","timestamp":1762264248000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GeneRetinaNet: a weighted aligned pyramid structure chip text localization network based on Graph-based Recursive Convolution"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6899-8637","authenticated-orcid":false,"given":"Jie","family":"Cao","sequence":"first","affiliation":[]},{"given":"Song","family":"Cai","sequence":"additional","affiliation":[]},{"given":"Daolong","family":"Han","sequence":"additional","affiliation":[]},{"given":"Chao","family":"Dong","sequence":"additional","affiliation":[]},{"given":"Jianfeng","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Guihua","family":"Lu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,4]]},"reference":[{"key":"327_CR1","doi-asserted-by":"crossref","unstructured":"Atienza R (2021) Data augmentation for scene text recognition. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp 1561\u20131570","DOI":"10.1109\/ICCVW54120.2021.00181"},{"issue":"6","key":"327_CR2","doi-asserted-by":"publisher","first-page":"1383","DOI":"10.1016\/j.clinph.2019.11.029","volume":"131","author":"LN Ayton","year":"2020","unstructured":"Ayton LN, Barnes N, Dagnelie G, Fujikado T, Goetz G, Hornig R, Jones BW, Muqit MM, Rathbun DL, Stingl K et al (2020) An update on retinal prostheses. Clin Neurophysiol 131(6):1383\u20131398","journal-title":"Clin Neurophysiol"},{"key":"327_CR3","unstructured":"Cao J, Chen Q, Guo J, Shi R (2020) Attention-guided context feature pyramid network for object detection. arXiv:2005.11475"},{"key":"327_CR4","doi-asserted-by":"crossref","unstructured":"Cao J, Zhang J, Li B, Gao L, Zhang J (2023) Retinamot: rethinking anchor-free yolov5 for online multiple object tracking. Complex Intell Syst 1\u201319","DOI":"10.1007\/s40747-023-01009-3"},{"key":"327_CR5","unstructured":"Chen K, Cao Y, Loy CC, Lin D, Feichtenhofer C (2020) Feature pyramid grids. arXiv:2004.03580"},{"key":"327_CR6","doi-asserted-by":"crossref","unstructured":"Chollet F (2017) Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp 1251\u20131258","DOI":"10.1109\/CVPR.2017.195"},{"key":"327_CR7","doi-asserted-by":"crossref","unstructured":"Dai J, Qi H, Xiong Y, Li Y, Zhang G, Hu H, Wei Y (2017) Deformable convolutional networks. In: Proceedings of the IEEE international conference on computer vision. pp 764\u2013773","DOI":"10.1109\/ICCV.2017.89"},{"issue":"1","key":"327_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-72848-0","volume":"10","author":"A Deichler","year":"2020","unstructured":"Deichler A, Carrasco D, Lopez-Jury L, Vega-Zuniga T, M\u00e1rquez N, Mpodozis J, Mar\u00edn GJ (2020) A specialized reciprocal connectivity suggests a link between the mechanisms by which the superior colliculus and parabigeminal nucleus produce defensive behaviors in rodents. Sci Rep 10(1):1\u201316","journal-title":"Sci Rep"},{"issue":"9","key":"327_CR9","doi-asserted-by":"publisher","first-page":"3986","DOI":"10.1093\/cercor\/bhab064","volume":"31","author":"Y Gu","year":"2021","unstructured":"Gu Y, Sainburg LE, Kuang S, Han F, Williams JW, Liu Y, Zhang N, Zhang X, Leopold DA, Liu X (2021) Brain activity fluctuations propagate as waves traversing the cortical hierarchy. Cereb Cortex 31(9):3986\u20134005","journal-title":"Cereb Cortex"},{"key":"327_CR10","doi-asserted-by":"crossref","unstructured":"Guo C, Fan B, Zhang Q, Xiang S, Pan C (2020) Augfpn: Improving multi-scale feature learning for object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp 12595\u201312604","DOI":"10.1109\/CVPR42600.2020.01261"},{"key":"327_CR11","doi-asserted-by":"crossref","unstructured":"Gupta A, Vedaldi A, Zisserman A (2016) Synthetic data for text localisation in natural images. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 2315\u20132324","DOI":"10.1109\/CVPR.2016.254"},{"key":"327_CR12","doi-asserted-by":"crossref","unstructured":"He P, Huang W, He T, Zhu Q, Qiao Y, Li X (2017a) Single shot text detector with regional attention. In: Proceedings of the IEEE International Conference on Computer Vision. pp 3047\u20133055","DOI":"10.1109\/ICCV.2017.331"},{"key":"327_CR13","doi-asserted-by":"crossref","unstructured":"He W, Zhang X-Y, Yin F, Liu C-L (2017b) Deep direct regression for multi-oriented scene text detection. In: Proceedings of the IEEE International Conference on Computer Vision. pp 745\u2013753","DOI":"10.1109\/ICCV.2017.87"},{"key":"327_CR14","doi-asserted-by":"crossref","unstructured":"Huang M, Liu Y, Peng Z, Liu C, Lin D, Zhu S, Yuan N, Ding K, Jin L (2022) Swintextspotter: Scene text spotting via better synergy between text detection and text recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp 4593\u20134603","DOI":"10.1109\/CVPR52688.2022.00455"},{"issue":"18","key":"327_CR15","doi-asserted-by":"publisher","first-page":"3775","DOI":"10.3390\/app9183775","volume":"9","author":"M Ju","year":"2019","unstructured":"Ju M, Luo H, Wang Z, Hui B, Chang Z (2019) The application of improved yolo v3 in multi-scale target detection. Appl Sci 9(18):3775","journal-title":"Appl Sci"},{"key":"327_CR16","first-page":"11474","volume":"34","author":"M Liao","year":"2020","unstructured":"Liao M, Wan Z, Yao C, Chen K, Bai X (2020) Real-time scene text detection with differentiable binarization. Proc AAAI Conf Artif Intell 34:11474\u201311481","journal-title":"Proc AAAI Conf Artif Intell"},{"issue":"1","key":"327_CR17","doi-asserted-by":"publisher","first-page":"919","DOI":"10.1109\/TPAMI.2022.3155612","volume":"45","author":"M Liao","year":"2022","unstructured":"Liao M, Zou Z, Wan Z, Yao C, Bai X (2022) Real-time scene text detection with differentiable binarization and adaptive scale fusion. IEEE Trans Pattern Anal Mach Intell 45(1):919\u2013931","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"11","key":"327_CR18","first-page":"8048","volume":"44","author":"Y Liu","year":"2021","unstructured":"Liu Y, Shen C, Jin L, He T, Chen P, Liu C, Chen H (2021) Abcnet v2: adaptive Bezier-curve network for real-time end-to-end text spotting. IEEE Trans Pattern Anal Mach Intell 44(11):8048\u20138064","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"21","key":"327_CR19","doi-asserted-by":"publisher","first-page":"30685","DOI":"10.1007\/s11042-022-11940-1","volume":"81","author":"Y Luo","year":"2022","unstructured":"Luo Y, Cao X, Zhang J, Guo J, Shen H, Wang T, Feng Q (2022) Ce-fpn: enhancing channel information for object detection. Multim Tools Appl 81(21):30685\u201330704","journal-title":"Multim Tools Appl"},{"issue":"11","key":"327_CR20","doi-asserted-by":"publisher","first-page":"3111","DOI":"10.1109\/TMM.2018.2818020","volume":"20","author":"J Ma","year":"2018","unstructured":"Ma J, Shao W, Ye H, Wang L, Wang H, Zheng Y, Xue X (2018) Arbitrary-oriented scene text detection via rotation proposals. IEEE Trans Multimedia 20(11):3111\u20133122","journal-title":"IEEE Trans Multimedia"},{"key":"327_CR21","doi-asserted-by":"crossref","unstructured":"Pang J, Chen K, Shi J, Feng H, Ouyang W, Lin D (2019) Libra r-cnn: Towards balanced learning for object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp 821\u2013830","DOI":"10.1109\/CVPR.2019.00091"},{"issue":"13","key":"327_CR22","doi-asserted-by":"publisher","first-page":"10469","DOI":"10.1007\/s00521-021-06830-w","volume":"34","author":"I Rodriguez-Conde","year":"2022","unstructured":"Rodriguez-Conde I, Campos C, Fdez-Riverola F (2022) Optimized convolutional neural network architectures for efficient on-device vision-based object detection. Neural Comput Appl 34(13):10469\u201310501","journal-title":"Neural Comput Appl"},{"key":"327_CR23","doi-asserted-by":"crossref","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision. pp 618\u2013626","DOI":"10.1109\/ICCV.2017.74"},{"key":"327_CR24","doi-asserted-by":"crossref","unstructured":"Shi B, Bai X, Belongie S (2017) Detecting oriented text in natural images by linking segments. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp 2550\u20132558","DOI":"10.1109\/CVPR.2017.371"},{"key":"327_CR25","doi-asserted-by":"publisher","first-page":"108792","DOI":"10.1016\/j.patcog.2022.108792","volume":"130","author":"H Tang","year":"2022","unstructured":"Tang H, Yuan C, Li Z, Tang J (2022) Learning attention-guided pyramidal features for few-shot fine-grained recognition. Pattern Recogn 130:108792","journal-title":"Pattern Recogn"},{"key":"327_CR26","doi-asserted-by":"crossref","unstructured":"Tang H, Li Z, Peng Z, Tang J (2020) Blockmix: meta regularization and self-calibrated inference for metric-based meta-learning. In: Proceedings of the 28th ACM International Conference on Multimedia. pp 610\u2013618","DOI":"10.1145\/3394171.3413884"},{"key":"327_CR27","doi-asserted-by":"crossref","unstructured":"Tang H, Liu J, Yan S, Yan R, Li Z, Tang J (2023) M3net: multi-view encoding, matching, and fusion for few-shot fine-grained action recognition. In: Proceedings of the 31st ACM International Conference on Multimedia. pp 1719\u20131728","DOI":"10.1145\/3581783.3612221"},{"key":"327_CR28","doi-asserted-by":"crossref","unstructured":"Tang H, Li Z, Zhang D, He S, Tang J (2024) Divide-and-conquer: confluent triple-flow network for rgb-t salient object detection. IEEE Trans Pattern Anal Mach Intell","DOI":"10.1109\/TPAMI.2024.3511621"},{"key":"327_CR29","doi-asserted-by":"crossref","unstructured":"Tan M, Pang R, Le QV (2020) Efficientdet: Scalable and efficient object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp 10781\u201310790","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"327_CR30","doi-asserted-by":"crossref","unstructured":"Wang K, Liew JH, Zou Y, Zhou D, Feng J (2019) Panet: Few-shot image semantic segmentation with prototype alignment. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp 9197\u20139206","DOI":"10.1109\/ICCV.2019.00929"},{"key":"327_CR31","doi-asserted-by":"crossref","unstructured":"Wang W, Xie E, Li X, Hou W, Lu T, Yu G, Shao, S (2019a) Shape robust text detection with progressive scale expansion network. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9336\u20139345","DOI":"10.1109\/CVPR.2019.00956"},{"key":"327_CR32","doi-asserted-by":"crossref","unstructured":"Wang W, Xie E, Song X, Zang Y, Wang W, Lu T, Yu G, Shen C (2019b) Efficient and accurate arbitrary-shaped text detection with pixel aggregation network. In: Proceedings of the IEEE\/CVF international conference on computer vision. pp 8440\u20138449","DOI":"10.1109\/ICCV.2019.00853"},{"key":"327_CR33","doi-asserted-by":"crossref","unstructured":"Wang P, Zhang C, Qi F, Huang Z, En M, Han J, Liu J, Ding E, Shi G (2019) A single-shot arbitrarily-shaped text detector based on context attended multi-task learning. In: Proceedings of the 27th ACM International Conference on Multimedia. pp 1277\u20131285","DOI":"10.1145\/3343031.3350988"},{"key":"327_CR34","doi-asserted-by":"crossref","unstructured":"Xie Z, Huang Y, Zhu Y, Jin L, Liu Y, Xie L ((2019)) Aggregation cross-entropy for sequence recognition. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp 6538\u20136547","DOI":"10.1109\/CVPR.2019.00670"},{"key":"327_CR35","doi-asserted-by":"crossref","unstructured":"Xie C, Xia C, Ma M, Zhao Z, Chen X, Li J (2022) Pyramid grafting network for one-stage high resolution saliency detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp 11717\u201311726","DOI":"10.1109\/CVPR52688.2022.01142"},{"key":"327_CR36","unstructured":"Yang B, Bender G, Le QV, Ngiam J (2019) Condconv: Conditionally parameterized convolutions for efficient inference. Adv Neural Infor Process Syst 32"},{"key":"327_CR37","doi-asserted-by":"crossref","unstructured":"Zhang Q, Jiang Z, Lu Q, Han J, Zeng Z, Gao S-H, Men A (2020) Split to be slim: an overlooked redundancy in vanilla convolution. arXiv:2006.12085","DOI":"10.24963\/ijcai.2020\/442"},{"key":"327_CR38","doi-asserted-by":"crossref","unstructured":"Zhang D, Zhang H, Tang J, Wang M, Hua X, Sun Q (2020) Feature pyramid transformer. In: Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XXVIII 16. Springer, pp 323\u2013339","DOI":"10.1007\/978-3-030-58604-1_20"},{"key":"327_CR39","doi-asserted-by":"crossref","unstructured":"Zhao G, Ge W, Yu Y (2021) Graphfpn: Graph feature pyramid network for object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp 2763\u20132772","DOI":"10.1109\/ICCV48922.2021.00276"},{"key":"327_CR40","doi-asserted-by":"crossref","unstructured":"Zhou X, Yao C, Wen H, Wang Y, Zhou S, He W, Liang J (2017) East: an efficient and accurate scene text detector. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp 5551\u20135560","DOI":"10.1109\/CVPR.2017.283"},{"key":"327_CR41","doi-asserted-by":"publisher","first-page":"107336","DOI":"10.1016\/j.patcog.2020.107336","volume":"110","author":"Y Zhu","year":"2021","unstructured":"Zhu Y, Du J (2021) Textmountain: accurate scene text detection via instance segmentation. Pattern Recogn 110:107336","journal-title":"Pattern Recogn"},{"key":"327_CR42","doi-asserted-by":"crossref","unstructured":"Zhu Y, Chen J, Liang L, Kuang Z, Jin L, Zhang W (2021) Fourier contour embedding for arbitrary-shaped text detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp 3123\u20133131","DOI":"10.1109\/CVPR46437.2021.00314"}],"container-title":["Journal of King Saud University Computer and Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-025-00327-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44443-025-00327-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-025-00327-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T15:45:45Z","timestamp":1763567145000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44443-025-00327-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11]]},"references-count":42,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["327"],"URL":"https:\/\/doi.org\/10.1007\/s44443-025-00327-5","relation":{},"ISSN":["1319-1578","2213-1248"],"issn-type":[{"type":"print","value":"1319-1578"},{"type":"electronic","value":"2213-1248"}],"subject":[],"published":{"date-parts":[[2025,11]]},"assertion":[{"value":"11 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that there are no competing interests, financial or non-financial, that could be perceived as influencing the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}},{"value":"This study did not involve human participants, animal experiments, or sensitive biological materials requiring ethical approval. All public datasets utilized in this study\u2013including ICDAR2015, CIFAR-10, and SVHN\u2013have pre-completed anonymization processing (e.g., ICDAR2015 removes all personally identifiable information such as user-specific scene metadata, ensuring no individual privacy is involved). Additionally, the use of these datasets strictly complies with their official license terms: ICDAR2015 adheres to the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license, CIFAR-10 and SVHN follow the MIT License, and no commercial or unauthorized derivative use is conducted. The above practices fully meet the ethical standards for public dataset utilization in computer vision research.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}}],"article-number":"298"}}