Offline Malayalam Character Recognition: A Comparative Study Using Multiple Classifier Combination Techniques

2016 ◽  
pp. 69-77
Author(s):  
Anitha Mary M. O. Chacko ◽  
K. S. Anil Kumar
Keyword(s):  
Comparative Study ◽  
Character Recognition ◽  
Classifier Combination ◽  
Multiple Classifier

METASYNTHETIC APPROACH FOR HANDWRITTEN CHINESE CHARACTER RECOGNITION

2002 ◽  
Vol 01 (04) ◽  
pp. 621-633 ◽  
Author(s):  
BAIHUA XIAO ◽  
CHUNHENG WANG ◽  
RUWEI DAI
Keyword(s):  
Supervised Learning ◽  
Character Recognition ◽  
Chinese Character ◽  
Substantial Improvement ◽  
Classifier Combination ◽  
Chinese Character Recognition ◽  
Handwritten Chinese Character Recognition ◽  
Multi Stage ◽  
Multiple Classifier ◽  
Overall Performance

The metasynthetic approach for solving complicated problems was proposed in 1990.1 And the characteristics of metasynthetic approach can be summarized as human-machine cooperation and integration. Directed by the idea of metasynthesis, the design of two kinds of handwritten Chinese character recognition systems are given in this article. All the designs focus on incorporating human knowledge into multiple classifier combination, which is different from conventional integration. The first one is multi-stage adaptive weighted multiple classifier combination, in which a neural network for coefficient predicting is trained by supervised learning to provide weights suitable for the input pattern. And the second scheme is based on totally parallel combination, in which human intelligence and computer capabilities are combined together through multi-step supervised learning. The experimental results demonstrate substantial improvement in overall performance for handwritten Chinese character recognition with thousands of classes that must be discriminated.

scholarly journals Which OCR toolset is good and why? A comparative study

2021 ◽  
Vol 48 (2) ◽  
Author(s):  
Pooja Jain ◽  
◽  
Dr. Kavita Taneja ◽  
Dr. Harmunish Taneja ◽  
◽  
...  
Keyword(s):  
Comparative Study ◽  
Language Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Research Area ◽  
Real World Applications ◽  
Banking Education ◽  
Active Research ◽  
Active Research Area ◽  
Computational Technology

Optical Character Recognition (OCR) is a very active research area in many challenging fields like pattern recognition, natural language processing (NLP), computer vision, biomedical informatics, machine learning (ML), and artificial intelligence (AI). This computational technology extracts the text in an editable format (MS Word/Excel, text files, etc.) from PDF files, scanned or hand-written documents, images (photographs, advertisements, and alike), etc. for further processing and has been utilized in many real-world applications including banking, education, insurance, finance, healthcare and keyword-based search in documents, etc. Many OCR toolsets are available under various categories, including open-source, proprietary, and online services. This research paper provides a comparative study of various OCR toolsets considering a variety of parameters.

Combining Classifiers and Learning Mixture-of-Experts

2011 ◽  
pp. 318-326 ◽  
Author(s):  
Lei Xu ◽  
Shun-ichi Amari
Keyword(s):  
Fuzzy System ◽  
Character Recognition ◽  
Parametric Models ◽  
Classifier Combination ◽  
Single Individual ◽  
Learning Models ◽  
Mixture Of Experts ◽  
Combination Rules ◽  
Multiple Classifiers ◽  
Special Case

Expert combination is a classic strategy that has been widely used in various problem solving tasks. A team of individuals with diverse and complementary skills tackle a task jointly such that a performance better than any single individual can make is achieved via integrating the strengths of individuals. Started from the late 1980’ in the handwritten character recognition literature, studies have been made on combining multiple classifiers. Also from the early 1990’ in the fields of neural networks and machine learning, efforts have been made under the name of ensemble learning or mixture of experts on how to learn jointly a mixture of experts (parametric models) and a combining strategy for integrating them in an optimal sense. The article aims at a general sketch of two streams of studies, not only with a re-elaboration of essential tasks, basic ingredients, and typical combining rules, but also with a general combination framework (especially one concise and more useful one-parameter modulated special case, called a-integration) suggested to unify a number of typical classifier combination rules and several mixture based learning models, as well as max rule and min rule used in the literature on fuzzy system.

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