International Journal of Computer Engineering and Applications, Volume XII, Special Issue, March 18, ISSN

Transcription

1 International Journal of Computer Engineering and Applications, Volume XII, Special Issue, March 18, ISSN COMBINING GENETIC ALGORITHM WITH OTHER MACHINE LEARNING ALGORITHM FOR CHARACTER RECOGNITION Nidhi Kela 1, Sharmishta Desai 2 1 Department of Computer Engineering, MIT College of Engineering, Pune nidhikela31@gmail.com 2 Department of Computer Engineering, MIT College of Engineering, Pune sharmishta.desai@mitcoe.edu.in ABSTRACT: Analyzing data from images or from hand written text is a challenging task. The most important components are (i) character recognition and (ii) text detection from images. In this paper, we have taken Artificial Character dataset. In this system we have proposed the classification of data by using the genetic algorithm. Genetic algorithm is the recommended for the strong rule based classifier, and it is used for the mining the small set of data. Traditionally, there are different classification algorithm used like Decision Tree, Naive Bayes, etc. But, if these algorithms are applied with optimization of attributes then they perform best. In this system, a Genetic Algorithm based approach is proposed for artificial recognition of character, which makes the classification task simpler and accurate when compared with naïve bayes algorithm.. Keywords: Genetic Algorithms, Learning Classifier Systems, Feature Learning, Genetics- Based Machine Learning. [1] INTRODUCTION Character Recognition has various practical applications and is considered to be one of the most attractive areas of pattern recognition. Using this technique, interface between human and machine can be improved. The mechanism analyse the hand written text which consists of various characters which needs to be recognized. The main purpose of this paper is to take English characters as input, process it, train the system, to recognize the character and produce the output. The produced output can then Nidhi Kela and Sharmishta Desai 1

2 COMBINING GENETIC ALGORITHM WITH OTHER MACHINE LEARNING ALGORITHM FOR CHARACTER RECOGNITION be modified if required. The characters of English language are only recognized here but it can be further developed to recognize the characters of different languages as well.[10] The system implemented has four different steps. Initially pre-processing is done which amplifies the image in advance to processing. Next step is segmentation which helps in locating each of the individual characters and its boundaries. Here line segmentation, word segmentation and character segmentation is performed so that individual segmentation of characters is achieved. The third step is to identify the features of the individual characters. The final step is the classification. Application of Character Recognition It can be used for number plate recognition In this paper the literature review are explained in section II. The proposed approach modules description are explained in section III, experimental setup and result analysis are explained in section IV and at last it provides a conclusion in section V. [2] LITERATURE SURVEY Aitkenhead, M.J[1], uses decision trees concept, categorizing datasets provides a rapid and effective method.co-evolving is used for competition the decision tree and the training data set. This method is tested by using two distinct datasets and gives result set which is comparable with or superior to other classification methods. Beringer, J, Hüllermeier, E [2] Defines the key problem in stream mining and it is to be expanded by existence of machine learning and data mining methods. To meet increased requirements. Here Author develops an instance-based learning algorithm by considering the problem of classification on data streams. Dariusz Jankowski and Konrad Jackowski and Bogusław Cyganek[3] proposed algorithm Concept-adapting Evolutionary Algorithm For Decision Tree. Which does not require any Knowledge of the environment. Drift concept is used to classifying data stream in data mining. Author combines tree learner and evolutionary algorithm which gives high performance in term of accuracy and processing time. Domingos P., Hulten, G[4] proposed VFDT, VFDT is an anytime system that build decision trees by using constant memory and constant time. It uses Hoeffding bounds to obtained are nearly same as that of a conventional learner. Author applied VFDT to mining the continuous stream of Web access data from the whole University of Washington main campus. Gama, J. et al[5], uses concept of drift. In that relation between input and output data changes over time, it is an instance to online monetarized learning scenario. Here author uses adaptive learning strategies for handling draft. Adaptive learning system is basically used to remove the concept of draft from online data. Guan SU, ZhuCollard F[6], propsed incremental learning with ga s with different initialization schemes for incremental learning in classifier agents. By using incremental approach, a classifier agent can wholly utilize existing knowledge and instantly respond to changes in the environment. The proposed approach will speed up the learning process and improve classification rates. P. Vivekanandan R. Neduchezian,[7], used rule based models because it performs well in large datasets classification. GA is used to perform mining on small data sets. scalable and Nidhi Kela and Sharmishta Desai 2

3 International Journal of Computer Engineering and Applications, Volume XII, Special Issue, March 18, ISSN adaptable online genetic algorithm are proposed by author to mine classification rules with drift concept. Rakshana J. Shetty*, Nithin Kumar Heraje [10] predict the printed characters in a given information image and removing it. It is the procedure in which the characters are identified and perceived from a image. Optical character recognition for the organized English text is finished. The Machine Learning method is utilized where the framework is at first prepared for every one of the letters in order and quantities of the English language alongside the desired output. Adam Coates and et. al. [11] applied the different machine learning methods for extracting the features automatically from unlabeled data and proves that they allow to generate highly effective classifiers for both detection and recognition to be used in a high accuracy end to end system. [3] PROPOSED APPROACH [3.1] PROPOSED SYSTEM OVERVIEW Initially Hand Written Text is considered as a input data set. As the data contain redundant and noise data, therefore preprocessing of dataset is performed. In preprocessing stemming and stopword of the data is removed. After that testing file is uploaded and classification is done by using the OCR technique. Finally the result is compared with different classifier such as J48, naive bayes classifier. Figure: 1. Proposed System Overview. Nidhi Kela and Sharmishta Desai 3

4 COMBINING GENETIC ALGORITHM WITH OTHER MACHINE LEARNING ALGORITHM FOR CHARACTER RECOGNITION Following figure shows how preprocessing is done. The preprocessing solution is given to the segmentation method. Figure: 2. Optimization Solution [4] RESULT DISCUSSION [4.1] EXPERIMENTAL SETUP For implementing the system the following software is required: Window Operating system Python 3.6 Pycharm Weka 3.6 tool [4.2] DATABASE INFORMATION This database has been artificially generated by using a first order theory which describes the structure of ten capital letters of the English alphabet. The capital letters represented are as follows: A, C, D, E, F, G, H, L, P, R. Each instance is structured and is described by a set of segments (lines) which resemble the way an automatic program would segment an image. Each instance is stored in a separate file whose format is the following: CLASS OBJNUM TYPE XX1 YY1 XX2 YY2 SIZE DIAG For Eg :- For a1 1 0 line line line line line Nidhi Kela and Sharmishta Desai 4

5 International Journal of Computer Engineering and Applications, Volume XII, Special Issue, March 18, ISSN where CLASS is an integer number indicating the class, OBJNUM is an integer identifier of a segment (starting from 0) in the instance and the remaining columns represent attribute values. [4.3] ATTRIBUTE INFORMATION TYPE: the first attribute describes the type of segment and is always set to the string "line" and it is of the type char. XX1,YY1,XX2,YY2: these attributes contain the initial and final coordinates of a segment in a cartesian plane. It is of the type int. SIZE: this is the length of a segment computed by using the geometric distance between two points A(X1,Y1) and B(X2,Y2). Its type is float. DIAG: this is the length of the diagonal of the smallest rectangle which includes the picture of the character. The value of this attribute is the same in each object. Its type is float. [4.4] EXPECTED RESULT The following table contains the result of Artificial Character which is generated using weka tool. Classifier Time Taken(sec) Accuracy(%) Naïve Bayes Naïve Bayes with GA J J48 with GA Naïve Bayes with GA J48 with GA Table 1. Results using weka 1. Comparison with J48 Following figure 3 shows the comparison between J48 and genetic algorithm. It shows the time required for generating the result by using J48 and by using J48 with genetic algorithm. From the graph it shows that time required for classification by using J48 is more than the J48 with genetic algorithm. Nidhi Kela and Sharmishta Desai 5

6 COMBINING GENETIC ALGORITHM WITH OTHER MACHINE LEARNING ALGORITHM FOR CHARACTER RECOGNITION Fig 3. Time comparison for J48 classifier Following figure 4 shows the comparison between J48 and genetic algorithm. It shows the accuracy of J48 and J48 with genetic algorithm. Fig 4. Accuracy comparison for J48 classifier 2. Comparison with Naïve Bayes Following figure 5 shows the comparison between naïve bayes and naïve bayes with genetic algorithm. It shows the time required for generating the result by using Naïve bayes and by using Naïve bayes with genetic algorithm. Fig 5. Time comparison for Naïve Bayes classifier Nidhi Kela and Sharmishta Desai 6

7 International Journal of Computer Engineering and Applications, Volume XII, Special Issue, March 18, ISSN Following figure 6 shows the comparison between Naïve bayes and genetic algorithm. It shows the accuracy of Naïve bayes and Naïve bayes with genetic algorithm. Fig 6. Accuracy comparison for Naïve bayes classifier 3. Comparison with J48 and Naïve Bayes Following figure 7 shows the comparison between J48 with genetic algorithm and naïve bayes with genetic algorithm. It shows the time required for generating the result by using Naïve bayes and by using Naïve bayes with genetic algorithm. Fig 7. Time comparison for J48 and Naïve Bayes classifiers Following figure 8 shows the comparison between J48 with genetic algorithm and Naïve bayes with genetic algorithm. Fig 8. Accuracy comparison for J48 and Naïve bayes classifier Nidhi Kela and Sharmishta Desai 7

8 COMBINING GENETIC ALGORITHM WITH OTHER MACHINE LEARNING ALGORITHM FOR CHARACTER RECOGNITION [5] LIMITATIONS The identification of handwritten characters is lot more difficult than the formatted characters due to the variations in the human writing styles. [6] CONCLUSION AND FUTURE SCOPE In this paper, a proposed method is used to identify the character from given text. An important task is to preprocess the data accurately and segmentation is used to identify individual character. At last the classification is done for which gives the final result. Further advances can be done by using data of other languages. This can also be used for identifying the characters written on images. REFERENCES [1] Aitkenhead, M.J., A co-evolving decision tree classification method, Expert Syst. Appl. 34, 1, (2008). [2] Beringer, J., Hüllermeier, E., Efficient instance-based learning on data streams, Intell. Data Anal. 11, (2007). [3] Dariusz Jankowski and Konrad Jackowski and Bogusław Cyganek, Learning Decision Trees from Data Streams with Concept Drift, ICCS The International Conference on Computational Science, Volume 80, 2016, Pages [4] Domingos, P., Hulten, G., Mining high-speed data streams, Proc. sixth ACM SIGKDD Int. Conf. Knowl. Discov. data Min. KDD , (2000). [5] Gama, J. et al."a survey on concept drift adaptation, ACM Comput. Surv. 46, 4, 1 37 (2014). [6] Guan SU, ZhuCollard F (2005) An incremental approach to genetic-algorithms based classification. IEEE Trans Syst Man Cybern B 35(2): [7] P. Vivekanandan R.Neduchezian, Mining data streams with concept drifts using genetic algorithm, Artificial Intelligence Review, Springer- Verlag, Accepted Available online, 2011 [8] Notredame C, Higgins DG (1995). "SAGA a Genetic Algorithm for Multiple Sequence Alignment". Nucleic Acids Research. 24 (8): doi: /nar/ PMC PMID [9] [10] Rakshana J. Shetty*, Nithin Kumar Heraje Recognition of Formatted Text using Machine Learning Technique American Journal of Intelligent Systems 2017,7(3): [11] Adam Coates and et. al., Text detection and character recognition in scene images with unsupervised feature learning,. Nidhi Kela and Sharmishta Desai 8