A Method for Detecting and Recognizing Yi Character Based on Deep Learning

Aiming at the challenges associated with the absence of a labeled dataset for Yi characters and the complexity of Yi character detection and recognition,we present a deep learning-based approach for Yi character detection and recognition.In the detection stage,an improved Differentiable Binarization Network(DBNet)framework is introduced to detect Yi characters,in which the Omni-dimensional Dynamic Convolution(ODConv)is combined with the ResNet-18 feature extraction module to obtain multi-dimensional complementary features,thereby improving the accuracy of Yi character detection.Then,the feature pyramid network fusion module is used to further extract Yi character image features,improving target recognition at different scales.Further,the previously generated feature map is passed through a head network to produce two maps:a probability map and an adaptive threshold map of the same size as the original map.These maps are then subjected to a differentiable binarization process,resulting in an approximate binarization map.This map helps to identify the boundaries of the text boxes.Finally,the text detection box is generated after the post-processing stage.In the recognition stage,an improved lightweight MobileNetV3 framework is used to recognize the detect character regions,where the original Squeeze-and-Excitation(SE)block is replaced by the efficient Shuffle Attention(SA)that integrates spatial and channel attention,improving the accuracy of Yi characters recognition.Meanwhile,the use of depth separable convolution and reversible residual structure can reduce the number of parameters and computation of the model,so that the model can better understand the contextual information and improve the accuracy of text recognition.The experimental results illustrate that the proposed method achieves good results in detecting and recognizing Yi characters,with detection and recognition accuracy rates of 97.5%and 96.8%,respectively.And also,we have compared the detection and recognition algorithms proposed in this paper with other typical algorithms.In these comparisons,the proposed model achieves better detection and recognition results with a certain reliability.

明益王“秋波”琴的漆涂层材料与工艺分析

孙中山故居纪念馆馆藏一张明代落霞式古琴“秋波”,据考证为明代受封于江西抚州的益王监制。清代以后,它被广东的文人琴士评为广东四大名琴之一。运用剖面显微观察、扫描电镜能谱分析、显微拉曼、光学光热红外显微光谱、热裂解-色谱质谱分析等科学检测手段,对古琴脱落的大漆残片进行了无机和有机成分分析,并对其工艺进行了讨论。实验结果表明,大漆残片保存了两个时期的不同髹漆工艺,早期以蛋白质类和非/半干性油黏结剂混合黏土类无机矿物做灰胎,晚期以大漆和干性油黏结剂混合鹿角霜/骨灰做灰胎并掺加少量黄铜屑。将科学分析与古琴槽腹、琴背的铭文相印证,进一步明确了“秋波”琴明末斫髹、清末修复的流传历史。此外,运用盖蒂保护中心研发的ESCAPE数据分析方法,首次在中国传世古琴样品中明确鉴定出一般认为产自越南的野漆品种laccol,其为清代重修时广东中山的修琴师使用添加的。本研究立足于古琴文物实体,以详尽的科学实验为支撑,并参阅《太古遗音》《髹饰录》等文献,为明代益王琴的制作工艺提供了参考,同时为古琴文物的保护修复提供了科学支撑。

走出“义利之辩”主流话语的三大误区——让义与利回归各自准确的定位、定义和定性

"义利之辩"的传统主流话语,对于国人有关道义和利益等观念的形成,产生过深刻的影响。但由于其存在着对义与利的错误定位、定义和定性等理论缺陷,在其承担了一定的正面社会教化功能的同时,也在某种程度上导致了思想上的混乱,产生了不良的文化效应,不利于中国社会的现代化进程。因此,我们亟待走出传统误区,让义与利回归各自准确的定位、定义和定性,为有效进行利益整合并形成健康的利益格局,为有力推动社会发展、和谐与进步,开辟正确的理论通道。

性格化表演风格的追求与突破——苏菲·玛索与章子怡伦理片表演艺术特征比较

苏菲·玛索与章子怡在伦理片的表演中都突出了性格化表演的美学追求。在《我决定留下来》和《梅兰芳》中,她们二人分别以生活化和情感化的表演技巧,塑造了寻找真我与牺牲真爱的女性角色,体现了表演艺术风格的突破。

论茹志鹃对王安忆创作的影响

王安忆是现代文学史上非常有影响力的女性作家。而她的母亲茹志鹃也是一位著名的女性作家。纵观王安忆的创作,不难看出她的母亲茹志鹃对其创作的影响。这种影响既体现在王安忆创作的作品内容上,也体现在其创作方法上。总的来说,这种影响主要体现在三个方面,包括审美风格、叙事技巧和人物塑造。王安忆一方面不断继承和发展茹志鹃的创作技巧,另一方面也不断在其作品中对茹志鹃的人生经历进行重复和变形,最终形成自己独特的创作技巧和艺术风格。

伊水大鲵皮中胶原蛋白的提取及性质研究

伊水大鲵是指生活在伊河水系的大鲵,于2015年8月获批农业部农产品地理标识。为合理开发伊水大鲵产品,以0.5 mol/L的乙酸作为提取溶液,分别以木瓜蛋白酶(加酶量0.5%)、胃蛋白酶(加酶量198 U/g)作为提取用酶,液料比值15 m L/g,水解时间29 h,在4℃条件下对经过预处理的伊水大鲵皮中的胶原蛋白进行提取,提取率分别为12.42%和44.43%。2种方法提取的胶原蛋白的紫外最大吸收峰分别位于233 nm和232 nm处,符合胶原蛋白的紫外吸收特征。2种方法提取的胶原蛋白氨基酸组成分析表明,木瓜蛋白酶和胃蛋白酶提取的胶原蛋白的氨基酸组成相似,其中甘氨酸含量最高,谷氨酸、脯氨酸、羟脯氨酸的含量较高,木瓜蛋白酶提取的胶原蛋白以胱氨酸的含量最低;胃蛋白酶提取的胶原蛋白以酪氨酸的含量最低。