Jan Ben

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.

Adaptive long range vision in unstructured terrain

A novel probabilistic online learning framework for autonomous off-road robot navigation is proposed. The system is purely vision-based and is particularly designed for predicting traversability in unknown or rapidly changing environments. It uses self-supervised learning to quickly adapt to novel terrains after processing a small number of frames, and it can recognize terrain elements such as paths, man-made structures, and natural obstacles at ranges up to 30 meters. The system is developed on the LAGR mobile robot platform and the performance is evaluated using multiple metrics, including ground truth.

Speed-range dilemmas for vision-based navigation in unstructured terrain

Abstract The performance of vision-based navigation systems for off-road mobile robots depends crucially on the resolution of the camera, the sophistication of the visual processing, the latency between image and sensor capture to actuator control, and the period of the control loop. One particularly important design question is whether one should increase the resolution of the camera images, and the range of the obstacle detection algorithms, at the expense of latency and control loop period. We first report experimental results on the resolution-period trade-off with a stereo vision-based navigation system implemented on the LAGR mobile robot platform. We propose a multi-agent perception and control architecture that combines a sophisticated long-range path detection method operating at high resolution and low frame rate, with a simple stereo-based obstacle detection method operating at low resolution, high frame rate, and low latency. The system combines the advantages of the long-range module for strategic path planning, with the advantages of the short-range module for tactical driving.

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>>