|Title:||Image processing for automated form reading|
Optical character recognition devices
Optical pattern recognition
Hong Kong Polytechnic University -- Dissertations
Department of Electronic and Information Engineering
|Pages:||vi,  leaves : ill. ; 30 cm|
|Abstract:||Forms are used extensively to collect and distributed data. It is boring and time consuming for people to read a large volume of forms. The main task of the automatic form reading is to recognize the text in binarized images and extract the useful information. In this project, we aim to develop an efficient method for image processing for automatic form reading. Binarization of a gray scale document image is the first step and most important step for automatic form reading. The performance of a form reading system is highly dependent on the performance of its binarization. In this project, a new document image preprocessing and character segmentation system was developed. The system has two main parts, image preprocessing and character segmentation. The main task of image preprocessing is to extract text fields in a form image, which mainly includes image binarization, page segmentation, and text blocks and lines extraction. The main task of character segmentation is to segment the characters in text blocks for a recognition system. A two-stage binarization scheme with feedback was developed in this project, which combines a region-based binarization technique and a neural network based binarization technique. At first, the region-based binarization is performed. After the binarized image is obtained, the horizontal Run Length Smoothing Algorithm (RLSA) followed by 8-neightbour connection checking is adopted for page segmentation. This is followed by text block/line extraction based on several simple rules. Character segmentation based on the vertical projection and a peak-to-valley function is then carried out to extract isolate characters for a recognition system. Due to the possible selection of a wrong threshold level at the region-based binarization step, the text block may become too dark or too bright, which contains connected characters or broken characters. To solve this problem, we use the neural network based binarization (four neural netwoks used with each for a type of images) to re-binarized the problematic text blocks. After these text blocks are re-binarized, character segmentation is carried out again. By using the second stage binarization, most errors in the region-based binarization can be corrected. Experimental results on a number of test images show that our two-stage binarization performs better than other single-stage binarization in terms of binarization quality and computing time.|
|Rights:||All rights reserved|
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