Farsi Handwritten Word Recognition Using Continuous Hidden Markov Models and Structural Features
نویسندگان
چکیده
FARSI HANDWRITTEN WORD RECOGNITION USING CONTINUOUS HIDDEN MARKOV MODELS AND STRUCTURAL FEATURES
منابع مشابه
Holistic Farsi handwritten word recognition using gradient features
In this paper we address the issue of recognizing Farsi handwritten words. Two types of gradient features are extracted from a sliding vertical stripe which sweeps across a word image. These are directional and intensity gradient features. The feature vector extracted from each stripe is then coded using the Self Organizing Map (SOM). In this method each word is modeled using the discrete Hidde...
متن کاملHolistic Farsi handwritten word recognition using gradient features
In this paper we address the issue of recognizing Farsi handwritten words. Two types fo gradient features are extracted from a sliding vertical stripe which sweeps across a word image. These are directional and intensity gradient features. The feature vector extracted from each stripe is then coded using the Self Organizing Map (SOM). In this method each word is modeled using the discrete Hidde...
متن کاملUnconstrained Farsi handwritten word recognition using fuzzy vector quantization and hidden Markov models
An unconstrained Farsi handwritten word recognition system based on fuzzy vector quantization (FVQ) and hidden Markov model (HMM) for reading city names in postal addresses is presented. Preprocessing techniques including binarization, noise removal, slope correction and baseline estimation are described. Each word image is represented by its contour information. The histogram of chain code slo...
متن کاملHandwritten Farsi (Arabic) word recognition: a holistic approach using discrete HMM
A holistic system for the recognition of handwritten Farsi/Arabic words using right}left discrete hidden Markov models (HMM) and Kohonen self-organizing vector quantization is presented. The histogram of chain-code directions of the image strips, scanned from right to left by a sliding window, is used as feature vectors. The neighborhood information preserved in the self-organizing feature map ...
متن کاملFarsi Handwritten Word Recognition Using Discrete HMM and Self- Organizing Feature Map
A holistic system for the recognition of handwritten Farsi/Arabic words using right-left discrete hidden Markov models (HMM) and Kohonen self-organizing vector quantization(SOFM/VQ) for reading city names in postal addresses is presented. Pre-processing techniques including binarization, noise removal and besieged in a circumferential rectangular are described. Each word image is scanned form r...
متن کامل