16 research outputs found
Using generative models for handwritten digit recognition
We describe a method of recognizing handwritten digits by fitting generative models that are built from deformable B-splines with Gaussian ``ink generators'' spaced along the length of the spline. The splines are adjusted using a novel elastic matching procedure based on the Expectation Maximization (EM) algorithm that maximizes the likelihood of the model generating the data. This approach has many advantages. (1) After identifying the model most likely to have generated the data, the system not only produces a classification of the digit but also a rich description of the instantiation parameters which can yield information such as the writing style. (2) During the process of explaining the image, generative models can perform recognition driven segmentation. (3) The method involves a relatively small number of parameters and hence training is relatively easy and fast. (4) Unlike many other recognition schemes it does not rely on some form of pre-normalization of input images, but can handle arbitrary scalings, translations and a limited degree of image rotation. We have demonstrated our method of fitting models to images does not get trapped in poor local minima. The main disadvantage of the method is it requires much more computation than more standard OCR techniques
BézierSketch: A Generative Model for Scalable Vector Sketches
The study of neural generative models of human sketches is a fascinating
contemporary modeling problem due to the links between sketch image generation
and the human drawing process. The landmark SketchRNN provided breakthrough by
sequentially generating sketches as a sequence of waypoints. However this leads
to low-resolution image generation, and failure to model long sketches. In this
paper we present B\'ezierSketch, a novel generative model for fully vector
sketches that are automatically scalable and high-resolution. To this end, we
first introduce a novel inverse graphics approach to stroke embedding that
trains an encoder to embed each stroke to its best fit B\'ezier curve. This
enables us to treat sketches as short sequences of paramaterized strokes and
thus train a recurrent sketch generator with greater capacity for longer
sketches, while producing scalable high-resolution results. We report
qualitative and quantitative results on the Quick, Draw! benchmark.Comment: Accepted as poster at ECCV 202