Machine Learning: Handwritten Character Recognition

Transcription

1 Machine Learning: Handwritten Character Recognition Ayush Bharti, Shivani Srivastava, Jyoti Singh, Swarnima Pandey (Research Scholar, Department of Computer Science & Engineering, Sagar Institute of Technology & Management) Mr. Alankar Srivastava (Assistant Professor, Department of Computer Science & Engineering, Sagar Institute of Technology & Management) ABSTRACT Machine Learning is widely used in Banking sector for various purposes such as Character recognition, AML, Fraud detection etc. Character Recognition finds a large application in banking sector, so we decided to do some research on the use and work done on character recognition. Character Recognition is of two types: Online and Offline character. The Off-line Handwritten Character Recognition focuses on recognizing character or words that had been recorded earlier in the form of scanned image of document. Off-line CR is relatively more complex and requires more research compared to on-line CR because of variability on size and writing style of handwritten characters by different individual at different times. INTRODUCTION Machine learning is an area of computer science that makes use of statistical techniques to provide computer systems the ability to "learn" ( i.e., progressively improve performance on a specific task) with data, without being explicitly programmed. The name machine learning was coined in 1959 by Arthur Samuel. Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data [1]. Handwritten character recognition (HCR) is the technique of converting the handwritten text into appropriate form that can be easily readable by machine. The major problem in handwritten character recognition (HCR) system is the variation of the handwriting styles, which can be completely different for different writers. The objective of HCR system is to implement user friendly computer assisted character representation that will allow successful extraction of characters from handwritten documents and to digitalize and translate the handwritten text into machine readable text. Handwritten character Recognition system can be divided into two categories [9]: On-line character recognition:- In this system, recognition is performed when characters are under creation. Off-line character recognition:- In this system, first handwritten documents are generated, scanned, stored in computer and than they are recognized. LITERATURE REVIEW Character recognition is the process of detecting and recognizing characters from input image and converts it into machine recognizable form. Character reorganization plays vital role in various areas like banking, postal department and so on this process can be divided in 2 categories:- Online character recognition Offline character recognition Offline CR can be further divided in two parts :- 320 Ayush Bharti, Shivani Srivastava, Jyoti Singh, Swarnima Pandey, Mr. Alankar Srivastava

2 Handwritten character Recognition Printed Character Recognition In offline mode Handwritten Characters are acquired by scanning the document or capturing photographs of document. It is more complex than online CR because of variability in writing style [9] Figure (a):block diagram of HCR There are some basic methodologies which can be used in the field of handwritten character recognition:- Data acquisition Process of obtaining a digitized data from a real world source is known as data acquisition there are different kinds of input devices which help in data acquisitions such as scanner, digital camera, web camera, PDA, camcorder etc(handwritten courtesy amount and signature on bank cheques using neural network) Preprocessing It is the process in which text can be extracted from the document scanner quality, scan resolution paper quality and many other factors can affect the accuracy of text. Segmentation In this section an image which is a sequence of character is made to breakdown into sub image that means character is divided into sub characters in this clip data is used so that image displayed is clipped at appropriate edges.( Recognition of Formatted Text using Machine Learning Technique). Feature Extraction Features can be extracted from the segmented digit and signature by using a rotation and size independent feature extraction method. CENTRE OF THE IMAGE:- Center of the image can be obtained by using following equation [2]:- Centre-x=width/2 Centre-y=height/2 Basically, it serves 2 purposes:- To extract properties that identify a character uniquely and to extract properties that can differentiate between similar characters [15]. 321 Ayush Bharti, Shivani Srivastava, Jyoti Singh, Swarnima Pandey, Mr. Alankar Srivastava

3 Figure (b): Extraction of the image of the character Classification Task of assigning the data to their corresponding classes is known as classification. There are different techniques which are used for classification for eg:-template Matching, Statistical Techniques, Structural Techniques, Neural Network, and Support Vector Machine. PROBLEM STATEMENT Handwritten characters differ depending upon the writer (similar or diverse). So there is forever a necessity to enlarge a proficient handwritten recognition system. HCR has some potential applications which generate the requirement for developing such schemes in an advanced manner. So to overcome the problem of accuracy and speed, Template Matching is one of the solutions that were suitable to implement in recognizing the character because of the simple algorithm that was used [13]. SOLUTION: TEMPLATE MATCHING Character Recognition by using Template Matching is a system prototype that useful to recognize the character or alphabet by comparing two images of the alphabet. It is the process of finding the location of a sub image called a template inside an image. Once a number of corresponding templates is found their centers are used as corresponding points to determine the registration parameters. Figure: Workflow of the Template Matching Algorithm Template matching involves determining similarities between a given template and windows of the same size in an image and identifying the window that produces the highest similarity measure [12]. 322 Ayush Bharti, Shivani Srivastava, Jyoti Singh, Swarnima Pandey, Mr. Alankar Srivastava

4 TEMPLATE MATCHING STEPS FOR RECOGNITION TM algorithm implements the following steps:- Firstly, the character image from the detected string is selected. After that, the image to the size of the first template is rescaled. After rescale the image to the size of the first template (original) image,the matching metric is Then the highest match found is stored. The index of the best match is stored as the recognized character. computed. Figure (a): Character Image TEMPLATE MATCHING ALGORITHM This process involves the use of a database of characters or templates. There exists a template for all possible input characters. For recognition to occur, the current input character is compared to each template to find either an exact match, or the template with the closest representation of the input character. If I(x,y) is the input character Tn(x,y) is the template n, then the matching function s(i,tn) will return a value indicating how well template n matches the input character [12].Figure(b) shows the Extraction of the image of the character. Character recognition is achieved by identifying which Tn gives the best value of matching function, s(i,tn). The method can only be successful if the input character can the stored templates are of the same or similar font. Template matching can be performed on binary, threshold characters or on gray-level characters [12]. Now after the process of noise removal and filtering the input image, it is matched with database of characters or templates. Figure(c): Highest match found of character image 323 Ayush Bharti, Shivani Srivastava, Jyoti Singh, Swarnima Pandey, Mr. Alankar Srivastava

5 CONCLUSION In this paper comparative study of various phases in character recognition has been carried out. From this literature review, Wavelet transform based features provides maximum classification accuracy compared to other methods. It can be concluded that selection of relevant feature extraction and classification techniques plays an important role in performance of CR system. It can be conclude that by using the technique that is Template Matching to recognize the character image is user-friendly and as a result the recognition process of this system become smoothly because of the steps that used in this system while recognizing the character. FUTURE SCOPE A lot of Research is still needed for exploiting new features to improve the current performance. We can use some features specific to the mostly confusing characters, to increase the recognition rate. Even though the above system could gives several advantages to the users, but this system prototype are still facing a number of limitations. So that, further research could be carried out for improve the system prototype into a better system. REFERENCES [1] Anish Talwar, Yogesh Kumar Machine Learning: An artificial intelligence Methodology International Journal Of Engineering and Computer Science ISSN: Volume 2 Issue 12, Dec.2013 Page No [2] Mohammad Badrul Alam, Mohammad Abu Yousuf, Md. Sohag Mia Handwritten Courtesy Amount and Signature Recognition on Bank Cheque using Neural Network International Journal of Computer Applications ( )Volume 118 No. 5, May [3] Siddhartha Banerjee, Bibek Ranjan GhoshArkaKundu Handwritten Character Recognition from Bank Cheque International Journal of Computer Sciences and Engineering Open Access Research Paper Volume-4, Special Issue- 1 E-ISSN: H [4] Miran Karic, Goran Martinovic Improving Offline Handwritten Digit Recognition Using Concavity-Based Features INT J COMPUT COMMUN, ISSN (2): , April. [5] Ingulkar Ashwini Suresh, Prof.P.P.Narwade Indian Currency Recognition and Verification Using Image Processing international Research Journal of Engineering and Technology(IRJET) Volume: 03Issue: 06 June-2016 e-issn: p-issn: [6] Samta Jain Goyal, Rajeev Goyal Feature Extraction based on Diagonal Direction for Handwritten Recognition System using Neural Network International Journal of Advanced Research in Computer Science and Software Engineering volume 5, Issue 4,April 2015 ISSN: X [7] Vijay Laxmi Sahu, Babita Kubde Offline Handwritten Character Recognition Techniques using Neural Network: A Review International Journal of Science and Research (IJSR), India Online ISSN: Volume 2 Issue 1, January 2013 [8] Karishma Patel, Mikita Gandhi Offline Handwritten Character Recognition: A Review International Journal of Scientific & Engineering Research,, May ISSN [9] Renjini L, Rubeena B A Comprehensive Study On Handwritten Character Recognition System OSR Journal of Computer Engineering (IOSR -JCE) e-issn: ,p-ISSN: , Volume 17, Issue 2, Ver. IV(Mar Apr.2015), PP [10] Rakshana J. Shetty Nithin Kumar Heraje Recognition of Formatted Text using Machine Learning Technique American Journal of Intelligent Systems 2017, 7(3): DOI: /j.ajis [11] Najmeh Samadiani, Hamid Hassanpour neural network-based approach for recognizing multi-font printed English characters Journal Of Electrical Systems And Information Technology 2 (2015) [12] Nadira Muda, Nik Kamariah, Nik Ismail, Siti Azami, Abu Bakar, Jasni Mohamad Zain Fakulti Sistem Komputer & Kejuruteraan Perisian Optical Character Recognition By Using Template Matching Universiti Malaysia Pahang Karung Berkunci 12, Kuantan, Pahang [13 Manoj Sonkusare and Narendra Sahu A SURVEY ON HANDWRITTEN CHARACTER RECOGNITION (HCR) TECHNIQUES FOR ENGLISH ALPHABETS Advances in Vision Computing: An International Journal (AVC) Vol.3, No.1, March Ayush Bharti, Shivani Srivastava, Jyoti Singh, Swarnima Pandey, Mr. Alankar Srivastava