光学人物角色实现发脾气文本感知、挖掘和识别

K. Pradeepa, M. Sivitha
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引用次数: 0

摘要

在发脾气的人物角色中发现文本是大量基于内容的人物角色精神分析工作的必要条件。在这个提名安排外科和哈士奇技术侦查文本问题在发脾气的人物角色。提出了一种灵活有效的裁剪算法,用于从人物角色中提取多头文本。相反,一些额外的触角,模拟文本是水平方向的,以手握文本的冲动偏好。首先用机器可触及的成分检测器渗透刺激角色。然后利用连通分量聚类方法根据最大偏差发现潜在文本域。每个连接组件的骨架用于将不同的文本字符串彼此进行排序。然后退火前景假释领域和影响是否每个领域缓和文本。每个前景的剥离、倾斜和外表可以从cc中计算出来,从而为退火的人物角色萌发文本/非文本分类器。在这种不完全显示文本的熟练度中,它也从人物角色中导出并识别文本,并将识别的文本存储到一个不统一的文件柜中,通过整合大量的关键改进,提出了一种新的基于CC聚块的发脾气文本探测技术,该技术最终扩展到比其他熟练度有实质性的性能改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optical persona realization of tantrum text sensing, excavation and recognition
Text spotting in tantrum personas is an consequential obligatory for galore content-based persona psychoanalysis chores. In this nominate arrangement an surgical and husky technique for sleuthing textual matter in tantrum personas. A libertine and efficacious lopping algorithm is premeditated to educe poly-headed text from an persona. Opposed to some extra feelers which simulate that text is horizontally-oriented to handgrip text of impulsive predilection. The stimulation persona is first percolated with machine-accessible ingredient feeler. Connected component clumping is then used to discover prospect text realms based on the supreme deviation. The skeleton of apiece connected component avails to assort the divergent text strings from apiece other. Then anneal prospect parole realms and influence whether apiece realm moderates text or not. The exfoliation, skewed, and semblance of apiece prospect can be reckoned from CCs, to germinate a text/non text classifier for annealed personas. In this proficiencies not entirely reveal text, it also educes from the persona and recognizes the text in conditions of storing the recognized paroles into a disunite file cabinet by integrating galore key betterments over tralatitious surviving proficiencies to nominate a novel CC clumping based tantrum text sleuthing technique, which finally extends to substantial performance betterment over the other emulous proficiencies.
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