Recognizing handwritten characters remains a critical and formidable challenge within the realm of computervision. Although considerable strides have been made in enhancing English handwritten character recognitionthr...Recognizing handwritten characters remains a critical and formidable challenge within the realm of computervision. Although considerable strides have been made in enhancing English handwritten character recognitionthrough various techniques, deciphering Arabic handwritten characters is particularly intricate. This complexityarises from the diverse array of writing styles among individuals, coupled with the various shapes that a singlecharacter can take when positioned differently within document images, rendering the task more perplexing. Inthis study, a novel segmentation method for Arabic handwritten scripts is suggested. This work aims to locatethe local minima of the vertical and diagonal word image densities to precisely identify the segmentation pointsbetween the cursive letters. The proposed method starts with pre-processing the word image without affectingits main features, then calculates the directions pixel density of the word image by scanning it vertically and fromangles 30° to 90° to count the pixel density fromall directions and address the problem of overlapping letters, whichis a commonly attitude in writing Arabic texts by many people. Local minima and thresholds are also determinedto identify the ideal segmentation area. The proposed technique is tested on samples obtained fromtwo datasets: Aself-curated image dataset and the IFN/ENIT dataset. The results demonstrate that the proposed method achievesa significant improvement in the proportions of cursive segmentation of 92.96% on our dataset, as well as 89.37%on the IFN/ENIT dataset.展开更多
Cursive text recognition of Arabic script-based languages like Urdu is extremely complicated due to its diverse and complex characteristics.Evolutionary approaches like genetic algorithms have been used in the past fo...Cursive text recognition of Arabic script-based languages like Urdu is extremely complicated due to its diverse and complex characteristics.Evolutionary approaches like genetic algorithms have been used in the past for various optimization as well as pattern recognition tasks,reporting exceptional results.The proposed Urdu ligature recognition system uses a genetic algorithm for optimization and recognition.Overall the proposed recognition system observes the processes of pre-processing,segmentation,feature extraction,hierarchical clustering,classification rules and genetic algorithm optimization and recognition.The pre-processing stage removes noise from the sentence images,whereas,in segmentation,the sentences are segmented into ligature components.Fifteen features are extracted from each of the segmented ligature images.Intra-feature hierarchical clustering is observed that results in clustered data.Next,classification rules are used for the representation of the clustered data.The genetic algorithm performs an optimization mechanism using multi-level sorting of the clustered data for improving the classification rules used for recognition of Urdu ligatures.Experiments conducted on the benchmark UPTI dataset for the proposed Urdu ligature recognition system yields promising results,achieving a recognition rate of 96.72%.展开更多
目的探讨与分析择期宫颈环扎联合间苯三酚治疗宫颈机能不全对孕妇妊娠结局的影响。方法方便选取2017年7月—2023年2月于日照市中心医院诊治的宫颈机能不全孕妇74例作为研究对象,根据随机数表法分为间苯三酚组37例与对照组37例。对照组...目的探讨与分析择期宫颈环扎联合间苯三酚治疗宫颈机能不全对孕妇妊娠结局的影响。方法方便选取2017年7月—2023年2月于日照市中心医院诊治的宫颈机能不全孕妇74例作为研究对象,根据随机数表法分为间苯三酚组37例与对照组37例。对照组给予择期宫颈环扎治疗,间苯三酚组在对照组治疗的基础上给予间苯三酚治疗,记录两组的妊娠结局。结果间苯三酚组的手术时间、术中出血量、术后住院天数均优于对照组,差异有统计学意义(P均<0.05);间苯三酚组分娩孕周明显多于对照组,差异有统计学意义(P<0.05);间苯三酚组的宫内感染率、晚期流产率、足月分娩率分别为5.41%、2.70%、94.59%,均优于对照组的21.62%、18.92%、78.38%,差异有统计学意义(χ^(2)=4.162、5.045、4.162,P均<0.05);间苯三酚组的围产儿存活率显著高于对照组,差异有统计学意义(P<0.05);且间苯三酚组存活围产儿的出生体质量、5 min Apgar评分、1 min Apgar评分均高于对照组,差异有统计学意义(P均<0.05)。结论择期宫颈环扎联合间苯三酚治疗宫颈机能不全能促进患者康复,延长分娩孕周,改善存活围产儿的出生结局。展开更多
文摘Recognizing handwritten characters remains a critical and formidable challenge within the realm of computervision. Although considerable strides have been made in enhancing English handwritten character recognitionthrough various techniques, deciphering Arabic handwritten characters is particularly intricate. This complexityarises from the diverse array of writing styles among individuals, coupled with the various shapes that a singlecharacter can take when positioned differently within document images, rendering the task more perplexing. Inthis study, a novel segmentation method for Arabic handwritten scripts is suggested. This work aims to locatethe local minima of the vertical and diagonal word image densities to precisely identify the segmentation pointsbetween the cursive letters. The proposed method starts with pre-processing the word image without affectingits main features, then calculates the directions pixel density of the word image by scanning it vertically and fromangles 30° to 90° to count the pixel density fromall directions and address the problem of overlapping letters, whichis a commonly attitude in writing Arabic texts by many people. Local minima and thresholds are also determinedto identify the ideal segmentation area. The proposed technique is tested on samples obtained fromtwo datasets: Aself-curated image dataset and the IFN/ENIT dataset. The results demonstrate that the proposed method achievesa significant improvement in the proportions of cursive segmentation of 92.96% on our dataset, as well as 89.37%on the IFN/ENIT dataset.
文摘Cursive text recognition of Arabic script-based languages like Urdu is extremely complicated due to its diverse and complex characteristics.Evolutionary approaches like genetic algorithms have been used in the past for various optimization as well as pattern recognition tasks,reporting exceptional results.The proposed Urdu ligature recognition system uses a genetic algorithm for optimization and recognition.Overall the proposed recognition system observes the processes of pre-processing,segmentation,feature extraction,hierarchical clustering,classification rules and genetic algorithm optimization and recognition.The pre-processing stage removes noise from the sentence images,whereas,in segmentation,the sentences are segmented into ligature components.Fifteen features are extracted from each of the segmented ligature images.Intra-feature hierarchical clustering is observed that results in clustered data.Next,classification rules are used for the representation of the clustered data.The genetic algorithm performs an optimization mechanism using multi-level sorting of the clustered data for improving the classification rules used for recognition of Urdu ligatures.Experiments conducted on the benchmark UPTI dataset for the proposed Urdu ligature recognition system yields promising results,achieving a recognition rate of 96.72%.
文摘目的探讨与分析择期宫颈环扎联合间苯三酚治疗宫颈机能不全对孕妇妊娠结局的影响。方法方便选取2017年7月—2023年2月于日照市中心医院诊治的宫颈机能不全孕妇74例作为研究对象,根据随机数表法分为间苯三酚组37例与对照组37例。对照组给予择期宫颈环扎治疗,间苯三酚组在对照组治疗的基础上给予间苯三酚治疗,记录两组的妊娠结局。结果间苯三酚组的手术时间、术中出血量、术后住院天数均优于对照组,差异有统计学意义(P均<0.05);间苯三酚组分娩孕周明显多于对照组,差异有统计学意义(P<0.05);间苯三酚组的宫内感染率、晚期流产率、足月分娩率分别为5.41%、2.70%、94.59%,均优于对照组的21.62%、18.92%、78.38%,差异有统计学意义(χ^(2)=4.162、5.045、4.162,P均<0.05);间苯三酚组的围产儿存活率显著高于对照组,差异有统计学意义(P<0.05);且间苯三酚组存活围产儿的出生体质量、5 min Apgar评分、1 min Apgar评分均高于对照组,差异有统计学意义(P均<0.05)。结论择期宫颈环扎联合间苯三酚治疗宫颈机能不全能促进患者康复,延长分娩孕周,改善存活围产儿的出生结局。
