Optimised CNN Architectures for Handwritten Arabic Character Recognition

Handwritten character recognition is considered challenging compared with machine-printed characters due to the different human writing styles.Arabic is morphologically rich,and its characters have a high similarity.The Arabic language includes 28 characters.Each character has up to four shapes according to its location in the word(at the beginning,middle,end,and isolated).This paper proposed 12 CNN architectures for recognizing handwritten Arabic characters.The proposed architectures were derived from the popular CNN architectures,such as VGG,ResNet,and Inception,to make them applicable to recognizing character-size images.The experimental results on three well-known datasets showed that the proposed architectures significantly enhanced the recognition rate compared to the baseline models.The experiments showed that data augmentation improved the models’accuracies on all tested datasets.The proposed model outperformed most of the existing approaches.The best achieved results were 93.05%,98.30%,and 96.88%on the HIJJA,AHCD,and AIA9K datasets.

Effect of Co on Solidification Characteristics and Microstructural Transformation of Non-equilibrium Solidified Cu-Ni Alloys

Non-equilibrium solidification structures of Cu55Ni45 and Cu55Ni43Co2 alloys were prepared by the molten glass purification cycle superheating method.The variation of the recalescence phenomenon with the degree of undercooling in the rapid solidification process was investigated using an infrared thermometer.The addition of the Co element affected the evolution of the recalescence phenomenon in Cu-Ni alloys.The images of the solid-liquid interface migration during the rapid solidification of supercooled melts were captured by using a high-speed camera.The solidification rate of Cu-Ni alloys,with the addition of Co elements,was explored.Finally,the grain refinement structure with low supercooling was characterised using electron backscatter diffraction(EBSD).The effect of Co on the microstructural evolution during nonequilibrium solidification of Cu-Ni alloys under conditions of small supercooling is investigated by comparing the microstructures of Cu55Ni45 and Cu55Ni43Co2 alloys.The experimental results show that the addition of a small amount of Co weakens the recalescence behaviour of the Cu55Ni45 alloy and significantly reduces the thermal strain in the rapid solidification phase.In the rapid solidification phase,the thermal strain is greatly reduced,and there is a significant increase in the characteristic undercooling degree.Furthermore,the addition of Co and the reduction of Cu not only result in a lower solidification rate of the alloy,but also contribute to the homogenisation of the grain size.

Instance Segmentation of Characters Recognized in Palmyrene Aramaic Inscriptions

This study presents a single-class and multi-class instance segmentation approach applied to ancient Palmyrene inscriptions,employing two state-of-the-art deep learning algorithms,namely YOLOv8 and Roboflow 3.0.The goal is to contribute to the preservation and understanding of historical texts,showcasing the potential of modern deep learning methods in archaeological research.Our research culminates in several key findings and scientific contributions.We comprehensively compare the performance of YOLOv8 and Roboflow 3.0 in the context of Palmyrene character segmentation—this comparative analysis mainly focuses on the strengths and weaknesses of each algorithm in this context.We also created and annotated an extensive dataset of Palmyrene inscriptions,a crucial resource for further research in the field.The dataset serves for training and evaluating the segmentation models.We employ comparative evaluation metrics to quantitatively assess the segmentation results,ensuring the reliability and reproducibility of our findings and we present custom visualization tools for predicted segmentation masks.Our study advances the state of the art in semi-automatic reading of Palmyrene inscriptions and establishes a benchmark for future research.The availability of the Palmyrene dataset and the insights into algorithm performance contribute to the broader understanding of historical text analysis.

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.

Fireworks Optimization with Deep Learning-Based Arabic Handwritten Characters Recognition Model

Handwritten character recognition becomes one of the challenging research matters.More studies were presented for recognizing letters of various languages.The availability of Arabic handwritten characters databases was confined.Almost a quarter of a billion people worldwide write and speak Arabic.More historical books and files indicate a vital data set for many Arab nationswritten in Arabic.Recently,Arabic handwritten character recognition(AHCR)has grabbed the attention and has become a difficult topic for pattern recognition and computer vision(CV).Therefore,this study develops fireworks optimizationwith the deep learning-based AHCR(FWODL-AHCR)technique.Themajor intention of the FWODL-AHCR technique is to recognize the distinct handwritten characters in the Arabic language.It initially pre-processes the handwritten images to improve their quality of them.Then,the RetinaNet-based deep convolutional neural network is applied as a feature extractor to produce feature vectors.Next,the deep echo state network(DESN)model is utilized to classify handwritten characters.Finally,the FWO algorithm is exploited as a hyperparameter tuning strategy to boost recognition performance.Various simulations in series were performed to exhibit the enhanced performance of the FWODL-AHCR technique.The comparison study portrayed the supremacy of the FWODL-AHCR technique over other approaches,with 99.91%and 98.94%on Hijja and AHCD datasets,respectively.

Audio2AB:Audio-driven collaborative generation of virtual character animation

Background Considerable research has been conducted in the areas of audio-driven virtual character gestures and facial animation with some degree of success.However,few methods exist for generating full-body animations,and the portability of virtual character gestures and facial animations has not received sufficient attention.Methods Therefore,we propose a deep-learning-based audio-to-animation-and-blendshape(Audio2AB)network that generates gesture animations and ARK it's 52 facial expression parameter blendshape weights based on audio,audio-corresponding text,emotion labels,and semantic relevance labels to generate parametric data for full-body animations.This parameterization method can be used to drive full-body animations of virtual characters and improve their portability.In the experiment,we first downsampled the gesture and facial data to achieve the same temporal resolution for the input,output,and facial data.The Audio2AB network then encoded the audio,audio-corresponding text,emotion labels,and semantic relevance labels,and then fused the text,emotion labels,and semantic relevance labels into the audio to obtain better audio features.Finally,we established links between the body,gestures,and facial decoders and generated the corresponding animation sequences through our proposed GAN-GF loss function.Results By using audio,audio-corresponding text,and emotional and semantic relevance labels as input,the trained Audio2AB network could generate gesture animation data containing blendshape weights.Therefore,different 3D virtual character animations could be created through parameterization.Conclusions The experimental results showed that the proposed method could generate significant gestures and facial animations.

A Novel 6G Scalable Blockchain Clustering-Based Computer Vision Character Detection for Mobile Images

6G is envisioned as the next generation of wireless communication technology,promising unprecedented data speeds,ultra-low Latency,and ubiquitous Connectivity.In tandem with these advancements,blockchain technology is leveraged to enhance computer vision applications’security,trustworthiness,and transparency.With the widespread use of mobile devices equipped with cameras,the ability to capture and recognize Chinese characters in natural scenes has become increasingly important.Blockchain can facilitate privacy-preserving mechanisms in applications where privacy is paramount,such as facial recognition or personal healthcare monitoring.Users can control their visual data and grant or revoke access as needed.Recognizing Chinese characters from images can provide convenience in various aspects of people’s lives.However,traditional Chinese character text recognition methods often need higher accuracy,leading to recognition failures or incorrect character identification.In contrast,computer vision technologies have significantly improved image recognition accuracy.This paper proposed a Secure end-to-end recognition system(SE2ERS)for Chinese characters in natural scenes based on convolutional neural networks(CNN)using 6G technology.The proposed SE2ERS model uses the Weighted Hyperbolic Curve Cryptograph(WHCC)of the secure data transmission in the 6G network with the blockchain model.The data transmission within the computer vision system,with a 6G gradient directional histogram(GDH),is employed for character estimation.With the deployment of WHCC and GDH in the constructed SE2ERS model,secure communication is achieved for the data transmission with the 6G network.The proposed SE2ERS compares the performance of traditional Chinese text recognition methods and data transmission environment with 6G communication.Experimental results demonstrate that SE2ERS achieves an average recognition accuracy of 88%for simple Chinese characters,compared to 81.2%with traditional methods.For complex Chinese characters,the average recognition accuracy improves to 84.4%with our system,compared to 72.8%with traditional methods.Additionally,deploying the WHCC model improves data security with the increased data encryption rate complexity of∼12&higher than the traditional techniques.

Frontier Progress of Landscape Character Assessment in China

The role of Landscape Character Assessment(LCA)at the level of territorial landscape governance spans both natural and social sciences.By analyzing the development history,research distribution,methods and applications of cutting-edge cases of LCA in China,the following conclusions are drawn:①the LCA research in China originated earlier than that in Europe,but has not yet been systematically applied to the implementation of urban and rural planning at all levels;②the fundamental theory of LCA in China has been well constructed,with three main research directions:technologyled,assessment-led,and assessment combined with other theories;③the development of LCA in rural areas is more mature than in urban areas,but the progress of research is uneven across regions;④the current research presents significant“bottom-up”academic characteristics,and there is an urgent need for government decision-making authorities and academia to jointly promote a“top-down”standardized governance mechanism to comprehensively promote the modernization of territorial landscape governance.

Progress in the evaluation tool of Character Strengths and its application in nursing staff

Character Strengths is a group of positive personality traits reflected through cognition,behavior,and emotion,which play a positive role in improving happiness,alleviating negative emotions,and maintaining physical and mental health.This article reviews the concept,content,methods,evaluation tools,and application progress of Character Strengths in nurses,so as to provide a reference for clinical managers and improve the quality of life,mental health,and professional satisfaction of n urses.

Chip Surface Character Recognition Based on OpenCV

Chip surface character recognition is an important part of quality inspection in the field of microelectronics manufacturing.By recognizing the character information on the chip,automated production,quality control,and data collection and analysis can be achieved.This article studies a chip surface character recognition method based on the OpenCV vision library.Firstly,the obtained chip images are preprocessed.Secondly,the template matching method is used to locate the chip position.In addition,the surface characters on the chip are individually segmented,and each character image is extracted separately.Finally,a Support Vector Machine(SVM)is used to classify and recognize characters.The results show that this method can accurately recognize the surface characters of chips and meet the requirements of chip quality inspection.

A Review of Research on Handwritten Chinese Character Recognition with Multi-Feature Fusion

This paper analyzes the progress of handwritten Chinese character recognition technology,from two perspectives:traditional recognition methods and deep learning-based recognition methods.Firstly,the complexity of Chinese character recognition is pointed out,including its numerous categories,complex structure,and the problem of similar characters,especially the variability of handwritten Chinese characters.Subsequently,recognition methods based on feature optimization,model optimization,and fusion techniques are highlighted.The fusion studies between feature optimization and model improvement are further explored,and these studies further enhance the recognition effect through complementary advantages.Finally,the article summarizes the current challenges of Chinese character recognition technology,including accuracy improvement,model complexity,and real-time problems,and looks forward to future research directions.

Effect of Conventional and Pulsed TIG Welding on Microstructural and Mechanical Characteristics of AA 6082-T6 Repair Welds

Repair welding of AA 6082-T6 joints was carried out using ER 4043 filler through the TIG welding process with or without pulsed current.Microstructure and mechanical characteristics of the joints before and after repairing were investigated by examining macrostructure,microstructure,and distributions of porosity in the weld metal(WM),and by hardness,tensile,and bending tests.We observed that the welding current,phase transformations in heat-affected zone(HAZ)and porosity introduced in the WM during welding influence on its mechanical properties in sequence.The experimental results showed that the bead width and penetration as well as size of pores in the joints were mainly influenced by the welding currents.The sound joints were obtained at a welding current of 140 A with or without pulsed current when welding speed and gas flow rate were set at 20 cm·min-1 and 15 L·min-1,respectively.Among them,the decrease in mechanical properties of repair weld(RW)was directly related to the phase transformations in the over-ageing zone due to the double welding thermal cycles and elevated distribution of porosity in the WM.In addition,it was observed that the comparatively smaller grain size and lower porosity in WM of the RW produced by pulsed TIG welding gave a positive effect on its mechanical properties.

Economic Characters of Thirty-three Chinese Rice Cultivars in Angola

[Objective] The aim was to research economic characters of thirty-three Chinese rice cultivars in Angola.[Method] In the research,twenty-eight hybrid mid-indica rice cultivars and five conventional Japonica rice cultivars were classified into three groups and then grown in Luanda to analyze adaptability,fertility and major economic characters of the rice in rainy season.[Result] The research showed that of twenty-eight hybrid mid-indica rice cultivars,ten cultivars were of better adaptability and fertility,which are suitable to be grown in Luanda in Angola;productive ear number,total grain per ear,thousand-seed weight and ratio of grain to straw of the rice cultivars performed normally,but the whole developmental period shortened and seedling setting rate improved significantly.In addition,hybrid indica Teyou 721,Quanxiangyou 512,II you 128 and 102S/4HZ021 were of fertility potential at 11.5 t/hm2,which were all proved excellent in related characters in rainy season in Luanda of Angola.For conventional mid-season rice indica cultivars,Wandao 51 and Huanghuazhan were of better adaptability and fertility.In contrast,5 Japonica rice cultivars were of poor adaptability.[Conclusion] The research provided technical references for Chinese rice growing in Angola.

6082-T6铝合金构件及其超大跨空间结构的受力性能与设计方法

针对6082-T6铝合金,从材料力学性能、轴压与偏压构件稳定承载力、受弯构件受力性能方面进行了系统试验和数值仿真研究;建立了国产铝合金构件试验数据库,评估了中、欧、美规范及DSM、CSM共5种设计方法的可靠度水平;对6082-T6铝合金圆管及泡沫铝填充复合管开展侧向冲击试验与数值仿真研究,提出了构件动态响应计算方法,并建立了冲击下构件变形的预测方法;针对超大跨度铝合金巨型网格结构开展了探索性研究,提出了适用于超大跨度结构的新型节点形式及稳定受力性能分析。研究成果将对6082-T6铝合金构件及结构在工程中的推广应用、提升结构的承载与抗冲击防御能力提供依据与技术支撑。

6A01-T5铝合金/304不锈钢的缝隙腐蚀行为研究

目的 研究6A01-T5铝合金和304不锈钢异种金属间的缝隙腐蚀行为规律。方法 采用常温常压浸泡腐蚀方法,结合SEM、EDS、XRD和白光干涉检测,对6A01-T5铝合金/304不锈钢缝隙结构在碱性Na Cl溶液中的腐蚀行为进行研究。结果 6A01-T5铝合金/304不锈钢缝隙腐蚀表现为在缝口处产生沿缝口方向的凹陷渠,在缝内,以局部减薄和腐蚀坑为主,并随着腐蚀时间的延长,腐蚀程度逐渐加重。不同区域腐蚀产物的形貌和成分组成不尽相同,缝外暴露区的腐蚀产物表现为晶体颗粒状,主要成分为Ca CO3和Al(OH)3等铝的腐蚀产物。缝口附近沿缝口方向形成一层有明显界线的腐蚀产物覆盖层,缝隙内部的腐蚀产物相对较少,表现为尺寸相对较小的结块,主要成分为Al(OH)3等铝的腐蚀产物。结论 在6A01-T5铝合金与304不锈钢非电连接情况下,6A01-T5铝合金/304不锈钢缝隙内2种金属分别独立发生缝隙腐蚀,因2种金属在腐蚀过程中争夺O2,使得304不锈钢对6A01-T5铝合金的缝隙腐蚀起到抑制作用。

Arabic Optical Character Recognition:A Review

This study aims to review the latest contributions in Arabic Optical Character Recognition(OCR)during the last decade,which helps interested researchers know the existing techniques and extend or adapt them accordingly.The study describes the characteristics of the Arabic language,different types of OCR systems,different stages of the Arabic OCR system,the researcher’s contributions in each step,and the evaluationmetrics for OCR.The study reviews the existing datasets for the Arabic OCR and their characteristics.Additionally,this study implemented some preprocessing and segmentation stages of Arabic OCR.The study compares the performance of the existing methods in terms of recognition accuracy.In addition to researchers’OCRmethods,commercial and open-source systems are used in the comparison.The Arabic language is morphologically rich and written cursive with dots and diacritics above and under the characters.Most of the existing approaches in the literature were evaluated on isolated characters or isolated words under a controlled environment,and few approaches were tested on pagelevel scripts.Some comparative studies show that the accuracy of the existing Arabic OCR commercial systems is low,under 75%for printed text,and further improvement is needed.Moreover,most of the current approaches are offline OCR systems,and there is no remarkable contribution to online OCR systems.

摆动模式对7005-T6铝合金激光焊接头力学性能及腐蚀行为的影响

采用摆动激光焊对7005-T6铝合金进行焊接。利用光学显微镜、扫描电子显微镜、透射电子显微镜、电子背散射衍射和电化学工作站等研究了三种激光摆动模式对接头微观组织、力学性能和腐蚀行为的影响。结果表明:使用摆动激光焊可以有效改善焊缝成型和降低柱状晶区域宽度;引入摆动模式可以细化焊缝处晶粒,减少气孔缺陷,提高焊缝处硬度和抗拉强度;摆动模式下焊缝晶界析出相尺寸更小,分布更加弥散,降低了腐蚀倾向和明显减少剥落腐蚀失重,同时可减少剥落腐蚀影响深度。经综合性能对比,发现摆动模式优于无摆动,无穷模式优于三角模式。

6082-T1铝合金的热变形行为及组织演变

为了深入探究6082-T1铝合金热变形的微观机制,如动态再结晶过程的微观机制、晶粒细化行为以及其对最终材料性能的影响,采用Gleeble-1500热模拟试验机对6082-T1铝合金进行热压缩试验,研究了合金在变形温度420~510℃,应变速率0.1~10 s^(-1)条件下的热变形行为,基于Arrhenius双曲正弦本构关系建立本构方程,基于动态材料模型建立了热加工图,并研究了微观组织演变规律。结果表明,6082-T1铝合金在合适变形温度区间中,随着温度升高,小角度晶界逐渐转变为大角度晶界,为再结晶提供形核场所,从而增加再结晶晶粒的含量,再结晶的形核和长大消除了合金的晶格畸变,晶粒取向更有利于滑移,从而导致变形抗力减小。

挤压态6082-T6铝合金的高温拉伸力学性能及微观组织

通过Gleeble3500热模拟试验机研究了变形温度和应变速率对挤压态6082-T6铝合金高温拉伸力学性能的影响,采用光学显微镜(OM)、扫描电镜(SEM)和透射电镜(TEM)分析了合金在高温拉伸过程中的微观组织演变。结果表明:在恒定的应变速率下,挤压态6082-T6铝合金的拉伸强度随着拉伸温度的升高而下降;在恒定的拉伸温度下,其拉伸强度随着应变速率的升高而上升。挤压态6082-T6铝合金在高温(300~450℃)拉伸条件下表现为韧性断裂,在较高的变形温度和较低应变速率条件下,合金的韧窝增大且更深,表现出较好的塑性。在高温变形过程中,随着拉伸温度的升高,合金内部的位错密度下降,并出现了析出相粗化现象,导致合金的变形抗力下降。

7075-T6铝合金的放电等离子烧结连接工艺优化

采用放电等离子烧结技术对7075-T6铝合金进行连接,通过单因素法研究了母材表面预处理(400#砂纸打磨、化学浸蚀、400#砂纸打磨+化学浸蚀、2000#砂纸打磨+化学浸蚀)以及升温速率(10,30,50℃·min^(-1))、连接温度(450,470,490,510℃)、连接压力(4.5,5.0,5.5,6.0,7.0MPa)和保温时间(45,60,75,90,120min)对接头连接质量、剪切性能和变薄率的影响,确定了合适的表面预处理工艺以及最优的工艺参数。结果表明:合适的表面预处理工艺为400#砂纸打磨,获得7075-T6铝合金接头的连接质量最好,接头界面未闭合孔洞很少,焊合率最大,为71.7%;随着升温速率的升高,接头的连接质量变差,但其焊合率均高于76%,接头的抗剪强度和变薄率均降低;随着连接温度的升高或连接压力的增加,接头的连接质量变好,接头的焊合率、抗剪强度和变薄率均增大,但当连接压力大于6.0MPa时,焊合率和抗剪强度增加幅度减小,而变薄率大幅增加;随着保温时间的延长,接头的焊合率、抗剪强度和变薄率的变化幅度均较小。400#砂纸打磨后7075-T6铝合金的最优连接工艺为升温速率50℃·min^(-1)、连接压力6.0MPa、连接温度490℃、保温时间45min,此时接头的焊合率为81.3%,抗剪强度为132.9MPa,变薄率为1.91%,剪切断裂方式为韧性断裂。