Recognition of Persian Handwritten Numbers using LBP-HOG Descriptor
نویسندگان
چکیده
Recognition of handwritten numbers in any language is one of the most important issues attracted many researchers and this is because of the increasing importance of the issue in today's world and in applications such as automatic detection of mail addresses or identification of numbers of bank checks. Given the great interclass diversities and much extra-class similarities between some Persian handwritten numbers, solving the Persian handwritten numbers recognition problem is difficult. In this paper, a new method for recognizing Persian handwritten numbers using a combination of HOG and LBP descriptors is provided. The proposed descriptor enjoys significant advantages of which the important one is the recording of information and features relating to the image (by descriptor LBP)and yet the feature extraction of the image edges (by descriptor HOG). Another advantage of the proposed descriptor is the very small length of the feature vector and the fast calculations. To evaluate the effectiveness of the proposed method the standard data base HODA, a large database of 60,000 images, were used .Experiments carried out on the database show the high effectiveness of the proposed method (with an accuracy of 99.3%) in comparison with other similar methods.
منابع مشابه
Feature Level Fusion of WLBP and HOG for Hand Dorsal Vein Recognition
In this paper, a new approach is proposed to extract features from the dorsal hand vein pattern. The modified Weber Local Binary Pattern (WLBP) is feature descriptor extracted, which effectively combines the advantages of WLD and LBP. WLBP feature vector consists of two components: Differential Excitation and LBP. The Differential excitation component derived based on Weber's law, which extract...
متن کاملPersian Handwritten Digit Recognition Using Particle Swarm Probabilistic Neural Network
Handwritten digit recognition can be categorized as a classification problem. Probabilistic Neural Network (PNN) is one of the most effective and useful classifiers, which works based on Bayesian rule. In this paper, in order to recognize Persian (Farsi) handwritten digit recognition, a combination of intelligent clustering method and PNN has been utilized. Hoda database, which includes 80000 P...
متن کاملMoment-based local binary patterns: A novel descriptor for invariant pattern recognition applications
A novel descriptor able to improve the classification capabilities of a typical pattern recognition system is proposed in this paper. The introduced descriptor is derived by incorporating two efficient region descriptors, namely image moments and Local Binary Patterns (LBP), commonly used in pattern recognition applications, in the last decades. The main idea behind this novel feature extractio...
متن کامل