We describe an alternative approach to hand- printed word recognition using a hybrid of proce- dural and connectionist techniques. We utilize two connectionist components: one to concurrently make recognition and segmentation hypotheses, and another to perform refined recognition of segmented characters. Both networks are governed by a pro- cedural controller which incorporates systematic do- main knowledge and procedural algorithms to guide recognition. W e employ an approach wherein an image is pro- cessed over time by a spatiotemporal connectionist network. The scheme offers several attractive fea- tures including shift-invariance and retention of lo- cal spatial relationships along the dimension being temporalized, a reduction in the number of free pa- rameters, and the ability to process arbitrarily long images. Recognition results on a set of real-world isolated ZIP code digits are comparable to the best reported to date, with a 9 6 . 0 % recognition rate and a rate of 9 9 . 0 % w h e n 9.5% of the images are rejected.

Neural networks have been used to investigate some of the assumptions m a d e in Biederman's recognition by components (RBC) theory of visual perception. Biederman's RBC theory states, in part, that object vertices are critical features for the 2D region segmentation phase of human object recognition. This paper presents computational evidence for Biederman's claim that viewpoint-invariant vertices are critical to object recognition. In particular, w e present a neural network model for 2D object recognition using object vertices as image primitives. The neural net is able to recognize objects with as much as 65% mid-segment centered contour deletion, while it is unable to recognize objects with as little as 25% vertex centered deletion. In addition the neural net exhibits shift, scale and partial rotational invariance.