Cursive Handwriting Recognition

Transcription

1 March Cursive Handwriting Recognition

2 Introduction Cursive Handwriting Recognition has been an active area of research and due to its diverse enterprises applications, it continues to be a challenging research topic in terms of accuracy. The focus is specifically on recognition of cursive handwritten characters in insurance forms. Six different types of insurance forms have been used for the current study. These forms are mixtures of printed and handwritten text. Example fields: Phone number, SSN, telephone number, policy number, name, address, dependent details etc., 2

3 Objective To design a semi automated method for extracting the cursive handwritten text present in insurance forms and reduce the errors that could happen during extraction and recognition. Also it attempts to correct the recognition error using Natural Language Processing(NLP). 3

4 Challenges It is challenging task to design a practical cursive handwritten recognition system, which can maintain high accuracy and it is independent of quality of the input documents. Complexity of character segmentation stems from the wide variety of fonts, rapidly expanding text styles and poor image characteristics. Touched, overlapped, separated and broken characters are major factors for causing segmentation errors. 4

5 Methodology Utilization of MATLAB as a platform to provide efficient solution within short period of time. Using Image processing toolbox to process the captured image in order to extract the enhanced image snippets and segment it to characters. Using methods: Histogram of Orientated Gradients(HOG) and Principal Component Analysis (PCA) to construct the feature model. Computer Vision tool box is used to classify and recognize segmented characters. MATLAB utilities are used to build GUI for displaying the results and store the extracted data in database within MATLAB environment. 5

6 The Proposed Model Input Image (Scanned Insurance Form) Preprocessing (Snippet Extraction +segmentation) Feature Extraction ( HoG PCA) Classification and Recognition (Neural Network) Natural Language processing (Error Correction) Store the Extracted data Manual Error Correction (Low confidence case) 6

7 Preprocessing The following series of operations needs to be perform on scanned input image. Image Enhancement Noise Filtering Smoothing the edges Normalization Dilation Tagging of Forms (Manual Process ) Snippet extraction Segmentation (Image snippet segment into individual glyphs or letters) Labeling process (Assigning a number to each segmented characters) 7

8 Feature Extraction HOG- PCA Features describes the relevant structural information contained in a pattern. Histograms of Oriented Gradient(HOG) descriptor and then project it to a linear subspace Principal Component Analysis(PCA). Robust under illumination, pose and view point changes. 8

9 Classification Neural Network (NN) is a powerful classifier that can be very useful to classify HOG-PCA features. Feed Forward Back Propagation Neural Network (FFBPN) is used. Neural Classifier consists of two hidden layers besides an input and output layer. The total number of neurons in output layer is 62 (upper case, lower case and numeric characters) as the proposed system is designed to identify numeric characters and alphabets. 9

10 Error Correction Using Natural Language processing (NLP) Building NLP aided Dictionary for insurance terms. It stores user terms extracted from the previous history (database) and then improves the recognition accuracy. It prevents repeated mis-recognitions. Also avoids the risk of user stress caused by repeated failures in recognition. It is used to modify the classification model. Helps to build adaptive classification model. 10

11 Tools used MATLAB R2013b : Generic math functions present in MATLAB MATLAB Tools/Functions Image Processing Tool Box Imhist, histeq, dilate, bwlabel, imadjust, histeq, adapthisteq, imfilter, imopen, imclose etc., Purpose Toolbox supports a wide range of image processing operations including noise filtering, histogram, enhancement, normalization etc., Computer Vision Tool Box extracthogfeatures Statistics Tool Box prepca, prestd, trapca etc., Neural Network Toolbox nntool Database Toolbox database, isconnection, set, sql2native To HOG features from the input image Principal component analysis on input data. To classify and correctly recognize objects based on the observed features To build database to store the extracted data Graphical User Interface(GUI) GUIDE To display the results within MATLAB Environment 11

12 Results Training Number of characters used for training (alphabets, numeric and alphanumeric) 20,000 Number of characters used for testing 15,000 Testing Numeric Recognition with accuracy of 96% at an average confidence level of 95% Alphabets with a accuracy of 81% with average confidence level of 85%. 12

13 Average ( Upper, Lower and Numeric) Character Recognition Results Hidden Units Classification rate(%) with NLP Classification rate(%) without NLP

14 Receiver Operating Characteristic plot Numeric Recognition Alphabets Recognition 14

15 Screen shots Graphical User Interface 15

16 Contd. 16

17 Conclusion In this work, a new NLP based cursive handwrite recognition approach has been presented in this project that produces promising results MATLAB tool is utilized to build a efficient cursive handwritten characters recognition system within short span of time NLP aided error correction helps to improve the accuracy significantly The recognition rate on this various insurance forms are very promising in the real time environment 17

18 Reference 1. Choudhary, A. (2014)A Review of Various Character Segmentation Techniques for Cursive Handwritten Words Recognition. 2. Abuzaraida, M. A., & Zeki, A. M. (2012, November). Recognition Techniques for Online Arabic Handwriting Recognition Systems. In Advanced Computer Science Applications and Technologies (ACSAT), 2012 International Conference on (pp ). IEEE. 3. Ghosh, R., & Ghosh, M. (2005). An intelligent offline handwriting recognition system using evolutionary neural learning algorithm and rule based over segmented data points. Journal of Research and Practice in Information Technology, 37(1), Günter, S. (2004). Multiple classifier systems in offline cursive handwriting recognition (Doctoral dissertation, University of Bern). 5. Wada, Y., & Kawato, M. (1995). A theory for cursive handwriting based on the minimization principle. Biological Cybernetics, 73(1),

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