New Feature Vector from Freeman Chain Code for Handwritten Roman Character Recognition
| Autor: | Fakhrul Syakirin Omar, Yenny Desnelita, Deni Yulianti, Teddy Chandra, Dewi Nasien, M. Hasmil Adiya |
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| Rok vydání: | 2018 |
| Předmět: |
Chain code
Landmark Artificial neural network business.industry Computer science Feature vector Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition 02 engineering and technology 01 natural sciences Support vector machine Character (mathematics) Computer Science::Computer Vision and Pattern Recognition 0103 physical sciences 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence 010306 general physics business Hidden Markov model |
| Zdroj: | 2018 2nd International Conference on Electrical Engineering and Informatics (ICon EEI). |
| Popis: | This paper proposes features that are extracted solely from Freeman Chain Code (FCC) for handwritten character recognition purpose. Targeting alphanumeric Roman characters, its structure constructed from the chain code is disassembled into segments and landmarks, before each segment is traced to detect predefined line shapes. Two types of feature vectors, sequentially connected shape identifiers and concurrently used shape occurrence counts and size ratios along with landmark positions, are produced from the tracing. Effectiveness of the proposed feature vectors are tested with Hidden Markov Model (HMM) for sequential, while concurrent feature vector is with Artificial Neural Network (ANN), showing mediocre results where only digit character class achieves the highest 80% classification accuracy. |
| Databáze: | OpenAIRE |
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