Craig R. Nohl

Shortest path segmentation: a method for training a neural network to recognize character strings

The authors describe a method which combines dynamic programming and a neural network recognizer for segmenting and recognizing character strings. The method selects the optimal consistent combination of cuts from a set of candidate cuts generated using heuristics. The optimal segmentation is found by representing the image, the candidate segments, and their scores as a graph in which the shortest path corresponds to the optimal interpretation. The scores are given by neural net outputs for each segment. A significant advantage of the method is that the labor required to segment images manually is eliminated. The system was trained on approximately 7000 unsegmented handwritten zip codes provided by the United States Postal Service. The system has achieved a per-zip-code raw recognition rate of 81% on a 2368 handwritten zip-code test set.<<ETX>>

OFF LINE RECOGNITION OF HANDWRITTEN POSTAL WORDS USING NEURAL NETWORKS

We describe a method, “Shortest Path Segmentation” (SPS), which combines dynamic programming and a neural net recognizer for segmenting and recognizing character strings. We describe the application of this method to two problems: recognition of handwritten ZIP Codes, and recognition of handwritten words. For the ZIP Codes, we also used the method to automatically segment the images during training: the dynamic programming stage both performs the segmentation and provides inputs and desired outputs to the neural network. Results are reported for a test set of 2642 unsegmented handwritten 212 dpi binary ZIP Code (5- and 9-digit) images. For handwritten word recognition, we combined SPS with a “Space Displacement Neural Network” approach, in which a single-character-recognition network is extended over the entire word image, and in which SPS techniques are then used to rank order a given lexicon. We report results on a test set of 3000 300 ppi gray scale word images, extracted from images of live mail pieces, for lexicons of size 10, 100, and 1000. Representing the problem as a graph as proposed in this paper has advantages beyond the efficient finding of the final optimal segmentation, or the automatic segmentation of images during training. We can also easily extend the technique to generate K “runner up” answers (for example, by finding the K shortest paths). This paper will also describe applications of some of these ideas.

Recognition-based segmentation of on-line run-on handprinted words: Input vs. output segmentation

Abstract The performance of two methods for recognition-based segmentation of strings of on-line handprinted capital Latin characters is reported. The input strings consist of a time-ordered sequence of X, Y coordinates, punctuated by pen-lifts. The methods are designed to work in “run-on mode” where there is no constraint on the spacing between characters. While both methods use a neural network recognition engine and a graph-algorithmic post-processor, their approaches to segmentation are quite different. The first method, which we call INSEG (for input segmentation), uses a combination of heuristics to identify particular pen-lifts as tentative segmentation points. The second method, which we call OUTSEG (for output segmentation), relies on the empirically trained recognition engine for both recognizing characters and identifying relevant segmentation points. The best results are obtained with the INSEG method: 11% error on handprinted words from an 80,000 word dictionary.

Analysis of complex and noisy check images

We describe an image analysis system for handling complex and noisy images of forms and bank documents, such as business checks, personal checks, or bank deposits. Some of these document types have no standardized layout, requiring a careful analysis of the whole image, to find out where the relevant information, for example the courtesy amount, is located. Each element in the image is first classified as being part of machine printed text, handwritten text, or as being a graphical element, such as a line. To obtain a reliable identification of these different elements under noisy conditions, a set of templates is scanned over the image, extracting such elements as strokes, line end stops and corners. From this representation a quick and robust analysis of the images content is possible to identify the different parts. Once a set of candidate subimages has been found, they are sent to a field recognition system. We describe an example of one such system, which locates and reads courtesy amounts on US checks.

A neural-net board system for machine vision applications

The authors describe a board system that integrates an analog neural net chip with a digital signal processor and fast memory. This system is in use as a coprocessor of a workstation where it accelerates computationally-intensive tasks for machine vision. A software environment has been developed to support image processing and testing of the system. The system was used to develop an application where the neural net determines the position and size of characters in complex images. For this task an increase in speed of a factor over 1000 over a workstation was achieved.<<ETX>>