Detail Hierarchical character recognition and its use in handwritten word/phrase recognition Bibliografi Author: Park, Jaehwa ; Srihari, Sargur N. (Advisor) Topik: COMPUTER SCIENCE|ENGINEERING; ELECTRONICS AND ELECTRICAL Bahasa: (EN )    ISBN: 0-599-61550-8     Penerbit: State University of New York     Tahun Terbit: 2000     Jenis: Theses - Dissertation Fulltext: 9958297.pdf (0.0B; 2 download) Abstract Off-line handwritten word/phrase recognition systems generally have monotonically cascaded architecture in these architectures, the recognition engine follows a static model with a fixed feature space. Built-in resources are exhaustively used at each stage of the serial engine regardless of input complexity For optimality and efficiency, a system that achieves maximum performance with minimum processing effort is desirable, and autonomous adaptation to input is one of the solutions to the goal. A recursive computational model for handwritten character/word/phrase recognition that has some similarities to the human cognitive approach is proposed. Two concepts, (i) altering recognition action using feedback and (ii) evaluating and regulating terminating conditions actively, are introduced for dynamic and interactive recognition. A hierarchical classification method is presented with dynamic usage of hierarchical feature space that preserves the benefits of the multi-resolution model. A lexicon-driven word recognizer which operates dynamically and has different degrees of classification ability is also presented. A concept of lexicon complexity derived from “matching transform distance” is utilized as a decision metric, which measures the difficulty of the given lexicon set with respect to the classification ability of the character recognizer. Recognition, decision making and recursive updating from the closed loop architecture is designed for a recursive recognition scenario. This proposed model recursively enhances the degree of classification until decision conditions are satisfied. A prune stroke analysis which detects the writer's spacing style is formulated to extend word recognition to phrase recognition system. The prime stroke period is used as a metric to limit the combination of strokes for word recognition and to generate phrase hypotheses. The proposed models achieve 98% and 96% of top choice correct rates in character and word recognition performance tested on NIST digit set and city name word images collected from the USPS mail stream respectively, Application of our phrase recognition to the recognition of street lines in USPS mail pieces resulted in a 100% improvement comparing to the performance of a word mode recognition. Opini Anda Klik untuk menuliskan opini Anda tentang koleksi ini! Lihat Sejarah Pengadaan  Konversi Metadata   Kembali

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