Showing papers by "Patrick Haffner published in 2001"

Gradient-based learning applied to document recognition

Abstract: Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional neural networks, which are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques. Real-life document recognition systems are composed of multiple modules including field extraction, segmentation recognition, and language modeling. A new learning paradigm, called graph transformer networks (GTN), allows such multimodule systems to be trained globally using gradient-based methods so as to minimize an overall performance measure. Two systems for online handwriting recognition are described. Experiments demonstrate the advantage of global training, and the flexibility of graph transformer networks. A graph transformer network for reading a bank cheque is also described. It uses convolutional neural network character recognizers combined with global training techniques to provide record accuracy on business and personal cheques. It is deployed commercially and reads several million cheques per day.

Efficient conversion of digital documents to multilayer raster formats

Abstract: How can we turn the description of a digital (i.e. electronically produced) document into something that is efficient for multi-layer raster formats? It is first shown that a foreground/background segmentation without overlapping foreground components can be more efficient for viewing or printing. Then, a new algorithm that prevents overlaps between foreground components while optimizing both the document quality and compression ratio is derived from the minimum description length (MDL) criterion. This algorithm makes the DjVu compression format significantly, more efficient on electronically produced documents. Comparisons with other formats are provided.

Escaping the Convex Hull with Extrapolated Vector Machines

Abstract: Maximum margin classifiers such as Support Vector Machines (SVMs) critically depends upon the convex hulls of the training samples of each class, as they implicitly search for the minimum distance between the convex hulls. We propose Extrapolated Vector Machines (XVMs) which rely on extrapolations outside these convex hulls. XVMs improve SVM generalization very significantly on the MNIST [7] OCR data. They share similarities with the Fisher discriminant: maximize the inter-class margin while minimizing the intra-class disparity.

Conversion of digital documents to multilayer raster formats

Abstract: How can we turn the description of a digital (i.e. electronically produced) document into something that is efficient for multi-layer raster formats? It is first shown that a foreground/background segmentation without overlapping foreground components can be more efficient for viewing or printing. Then, a new algorithm that prevents overlaps between foreground components while optimizing both the document quality and compression ratio is derived from the minimum description length (MDL) criterion. This algorithm makes the DjVu compression format significantly, more efficient on electronically produced documents. Comparisons with other formats are provided.

DJVU document browsing with on-demand loading and rendering of image components

Abstract: Image-based digital documents are composed of multiple pages, each of which may be composed of multiple components such as the text, pictures, background, and annotations. We describe the image structure and software architecture that allows the DjVu system to load and render the required components on demand while minimizing the bandwidth requirements, and the memory requirements in the client. DjVu document files are merely a list of enriched URLs that point to individual files (or file elements) that contain image components. Image components include: text images, background images, shape dictionaries shared by multiple pages, OCRed text, and several types of annotations. A multithreaded software architecture with smart caching allows individual components to be loaded and predecoded and rendered on-demand. Pages are pre-fetched or loaded on demand, allowing users to randomly access pages without downloading the entire document, and without the help of a byte server. Components that are shared accross pages (e.g. shape dictionnaries, or background layers) are loaded as required and cached This greatly reduces the overall bandwidth requirements. Shared dictionnaries allow 40% typical file size reduction for scanned bitonal documents at 300dpi. Compression ratios on scanned US patents at 300dpi are 5.2 to 10.2 times higher than GroupIV with shared dictionnaries and 3.6 to 8.5 times higher than GroupIV without shared dictionnaries.