Logo recognition system

The objective of this research is to develop computer software for recognizing a company logo. The system is called "Logo Recognition System or LRS". There are 2 parts of the LRS, namely: a client part and server part. The client part consists of common device, which is owned by the user, such as a mobile device, tablet and smart phone. On the client part, the LRS provides an easy graphic user interface for capturing a logo image. After that the device sends the logo image to the server part. The server part is a computer server, which does a process of recognizing with several algorithms and generates a result with a link of the logo's company website. The LRS consists of 4 components, namely: 1) Image Acquisition 2) Image Preprocessing 3) Image Recognition, and 4) Result Presentation. The system uses the python program to develop the logo recognition system on both client and server. The LRS can recognize the rotation image with any angle. The precision rate of the system is around 79.6 percent.

[1]  Hossein Pourghassem,et al.  A Novel Logo Detection and Recognition Framework for Separated Part Logos in Document Images , 2011 .

[2]  Karim Faez,et al.  A novel method for extracting and recognizing logos , 2012 .

[3]  Marcin Skoczylas Detection of positions and recognition of brand logos visible on images captured using mobile devices , 2014, 2014 International Conference and Exposition on Electrical and Power Engineering (EPE).

[4]  Alireza Alaei,et al.  A Complete Logo Detection/Recognition System for Document Images , 2014, 2014 11th IAPR International Workshop on Document Analysis Systems.

[5]  H. Pourghassem A Hierarchical Logo Detection and Recognition Algorithm Using Two-Stage Segmentation and Multiple Classifiers , 2012, 2012 Fourth International Conference on Computational Intelligence and Communication Networks.

[6]  Corneliu Florea,et al.  Local description using multi-scale complete rank transform for improved logo recognition , 2014, 2014 10th International Conference on Communications (COMM).

[7]  Qigang Gao,et al.  Logo recognition based on a novel pairwise classification approach , 2012, The 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP 2012).

[8]  Xiao Fei,et al.  A fast coarse-to-fine vehicle logo detection and recognition method , 2007, 2007 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[9]  Ye Sun,et al.  Vehicle Logo Recognition System Based on Convolutional Neural Networks With a Pretraining Strategy , 2015, IEEE Transactions on Intelligent Transportation Systems.

[10]  S. Karthikeyan,et al.  Enhancing logo matching and recognition using local features , 2014, International Conference on Information Communication and Embedded Systems (ICICES2014).

[11]  Mohammad Khajehzadeh,et al.  Economic Design of Foundation Using Harmony Search Algorithm , 2011 .

[12]  Giovanni Soda,et al.  Edge-backpropagation for noisy logo recognition , 2003, Pattern Recognit..

[13]  Wei Liang,et al.  Individualized matching based on logo density for scalable logo recognition , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[14]  Muriel Visani,et al.  Improving Logo Spotting and Matching for Document Categorization by a Post-Filter Based on Homography , 2013, 2013 12th International Conference on Document Analysis and Recognition.

[15]  Hanqing Lu,et al.  A coarse-to-fine logo recognition method in video streams , 2014, 2014 IEEE International Conference on Multimedia and Expo Workshops (ICMEW).

[16]  Richard Price,et al.  A generalized regression neural network for logo recognition , 2000, KES'2000. Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies. Proceedings (Cat. No.00TH8516).

[17]  Nagul Cooharojananone,et al.  Vehicle logo recognition based on interior structure using SIFT descriptor and neural network , 2014, 2014 International Conference on Information Science, Electronics and Electrical Engineering.