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RECOGNITION OF NUMERICAL DATA ON TAX FORMS

 

TITLE RECOGNITION OF NUMERICAL DATA ON TAX FORMS
AUTHOR BOONMA WANNABOOT
DEGREE MASTER OF SCIENCE PROGRAMME IN COMPUTER SCIENCE
FACULTY FACULTY OF SCIENCE
ADVISOR SUKANYA PHONGSUPHAP
CO-ADVISOR CHOMTIP PORNPANOMCHAI
 
ABSTRACT
This research project investigates a new approach to the recognition of numerical data appearing on tax documents. The proposed approach is able to recognize both handwritten and machine-printed numbers appearing on documents. The system uses an image processing technique that consists of five major steps: 1) Pre-processing using Gaussian filtering and Binarization; 2) Segmentation, which, in turn, consists of digit segmentation, broken digit restoration, and connected digit separation; 3) Feature extraction using Directional Distance Distribution, Concavities measurement, Crossing point, and Kirsch operators; 4) Feature selection using the Divergence value analysis; 5) Digit recognition using a Neural network classifier and the feature set from step 4. To evaluate the recognition accuracy of the proposed approach, a total of 16,000 digits from actual tax documents were used in the experiments. The data can be divided as following: the training data set contained 14,400 digits (7,200 machine printed digits and 7,200 handwritten digits) and the testing data set contained 1,600 digits (800 machine-printed digits and 800 handwritten digits). The results showed that accuracy rates of recognition were 100% and 98.13% for machine-printed digits and handwritten digits respectively. The number of features used by the classifier was 235 out of the 442 available features. The results indicate that the proposed method work efficiently for the recognition of numerical data on tax forms, particularly for machine-printed digits.
KEYWORD HANDWRITTEN NUMERICAL RECOGNITION/ MACHINEPRINTED NUMERICAL RECOGNITION/ FEATURE EXTRACTION/ FEATURE SELECTION/ NEURAL NETWORK

 

 

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