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Five-Layered Neural Fuzzy Closed-Loop Hybrid Control System with Compound Bayesian Decision-Making Process for Classification Cum Identification of Mixed Connective Conjunct Consonants and Numerals

机译:具有复合贝叶斯决策过程的五层神经模糊闭环混合控制系统,用于分类混合结合辅音和数字

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The OCR generation systems are most sophisticated active field and interesting conversional discovery for digitalization of handwritten and typed imprecise data into machine detectable characters. The fuzzy logic system processes the data with help of primary-based bunch set of knowledge. Fuzzy logic closed-loop system having very good successful rate of frame work for decision-based functions, can derive the fuzzy rules to build the decision-making procedure and detect the letters human to system to human. Artificial neural networks are compatible and the best area to solve the pattern cum text recognition tasks. The innovative combinational based characteristic of neural fuzzy-based closed-loop hybrid system proposing a five-layered approach with technical ideas, solutions solve the critical problems in the field of character, face, symbol recognition procedure, and estimating the density ratio. Recognition of single text, numbers is easy than the recognition of mixed connective conjunct consonants. Because of their variations, various handwritten pen-stroke pulses, tuning the initial and end position of each conjunct consonant, some consonants are connected and mixed with their left-cum right-side placed conjuncts, numerals, and symbols. Many languages such as Arabic, Hindi, Urdu, Telugu, and Tamil represent syllabic, symbol scripted form, and most of words formed with the mixed conjuncts, mixed cum touched consonants, mixed conjuncts with numerals in their representation. This research approach has proposed the five-layered neural fuzzy closed-loop hybrid system with compound Bayesian decision-making process holding good outcome for classification cum identification of mixed connective conjunct consonants with their numerals. The recognition process can start with categorizing total text into two forms; normal conjunct consonants and mixed connective conjunct consonants. The permutation futures of five-layered neural fuzzy closed-loop hybrid system represent inp
机译:OCR生成系统是最复杂的活动场和有趣的交易发现,用于将手写的数字化和键入的不精确数据分为机器可检测字符。模糊逻辑系统在基于初级束的知识的帮助下处理数据。模糊逻辑闭环系统具有非常好的帧工作率,用于基于决策功能,可以导出模糊规则来构建决策程序,并检测人类对人类的字母。人工神经网络兼容,解决模式暨文本识别任务的最佳区域。基于创新组合的基于神经模糊的闭环混合系统特征,提出了一种具有技术思想的五层方法,解决方案解决了字符,面部,符号识别过程领域的关键问题,以及估计密度比。识别单个文本,数字容易识别混合结合辅音。由于它们的变化,各种手写笔划脉冲,调整每个结合辅音的初始位置,一些辅音与其左侧右侧放置的混合,数字和符号混合。许多语言如阿拉伯语,印地语,乌尔都语,泰卢固语和泰米尔,代表了音节,符号脚本形式,大多数用混合的混合形成的单词,混合暨触摸的辅音,混合与标号的混合混合在其代表中。该研究方法提出了五层神经模糊闭环混合系统,具有复合贝叶斯决策过程,持有良好的分类暨识别混合结合辅音与其数字的良好结果。识别过程可以从分类为两种形式的总文本;正常的结合辅音和混合结合辅音。五层神经模糊闭环混合系统的排列期货代表INP

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