An optical character recognition (OCR) frame-work is developed and applied to handprinted numeric fields recognition. The numeric fields were extracted from binary images of VISA® credit card application forms. The images include personal identity numbers and telephone numbers. The proposed OCR framework is a cascaded neural networks. The first stage is a self-organizing feature map algorithm. The second stage maps distance values into allograph membership values using a gradient descent learning algorithm. The third stage is a multi-layer feedforward net-work. In this paper, we present experimental results which demonstrate the ability to read handprinted numeric fields. Experiments were performed on a test data set from the CCL/ITRI database which consists of over 90,390 handwritten numeric digits.
|Number of pages||6|
|Journal||Machine Vision and Applications|
|Publication status||Published - 1997 Jan 1|
All Science Journal Classification (ASJC) codes
- Hardware and Architecture
- Computer Vision and Pattern Recognition
- Computer Science Applications