Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  proposes a statistical-structural character modeling method based on Markov random fields (MRFs) for handwritten Chinese character recognition (HCCR). The stroke relationships of a Chinese character reflect its structure, which can be statistically represented by the neighborhood system and clique potentials within the MRF framework. Based on the prior knowledge of character structures,  design the neighborhood system that accounts for the most important stroke relationships.  penalize the structurally mismatched stroke relationships with MRFs using the prior clique potentials and derive the likelihood clique potentials from Gaussian mixture models, which encode the large variations of stroke relationships statistically. In the proposed HCCR system,  use the single-site likelihood clique potentials to extract many candidate strokes from character images and use the pair-site clique potentials to determine the best structural match beten the input candidate strokes and the MRF-based character models by relaxation labeling. The experiments on the Korea Advanced Institute of Science and Technology (KAIST) character database demonstrate that MRFs can statistically model character structures, and work ll in the HCCR system.
['Jia Zeng', 'Zhi-Qiang Liu']
I am writing a research paper on a idea Markov Random Field-Based Statistical Character Structure Modeling for Handwritten Chinese Character Recognition
[{"from": "human", "value": "I am writing a research paper on a idea Markov Random Field-Based Statistical Character Structure Modeling for Handwritten Chinese Character Recognition"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  proposes a statistical-structural character modeling method based on Markov random fields (MRFs) for handwritten Chinese character recognition (HCCR). The stroke relationships of a Chinese character reflect its structure, which can be statistically represented by the neighborhood system and clique potentials within the MRF framework. Based on the prior knowledge of character structures,  design the neighborhood system that accounts for the most important stroke relationships.  penalize the structurally mismatched stroke relationships with MRFs using the prior clique potentials and derive the likelihood clique potentials from Gaussian mixture models, which encode the large variations of stroke relationships statistically. In the proposed HCCR system,  use the single-site likelihood clique potentials to extract many candidate strokes from character images and use the pair-site clique potentials to determine the best structural match beten the input candidate strokes and the MRF-based character models by relaxation labeling. The experiments on the Korea Advanced Institute of Science and Technology (KAIST) character database demonstrate that MRFs can statistically model character structures, and work ll in the HCCR system."}]