Showing papers by "Patrick Haffner published in 1998"

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.

Boxlets: A Fast Convolution Algorithm for Signal Processing and Neural Networks

Abstract: Signal processing and pattern recognition algorithms make extensive use of convolution. In many cases, computational accuracy is not as important as computational speed. In feature extraction, for instance, the features of interest in a signal are usually quite distorted. This form of noise justifies some level of quantization in order to achieve faster feature extraction. Our approach consists of approximating regions of the signal with low degree polynomials, and then differentiating the resulting signals in order to obtain impulse functions (or derivatives of impulse functions). With this representation, convolution becomes extremely simple and can be implemented quite effectively. The true convolution can be recovered by integrating the result of the convolution. This method yields substantial speed up in feature extraction and is applicable to convolutional neural networks.

Image and video coding-emerging standards and beyond

Abstract: Discusses coding standards for still images and motion video. We first briefly discuss standards already in use, including: Group 3 and Group 4 for bilevel fax images; JPEG for still color images; and H.261, H.263, MPEG-1, and MPEG-2 for motion video. We then cover newly emerging standards such as JBIG1 and JBIG2 for bilevel fax images, JPEG-2000 for still color images, and H.263+ and MPEG-4 for motion video. Finally, we describe some directions beyond the standards such as hybrid coding of graphics/photo images, MPEG-7 for multimedia metadata, and possible new technologies.

Browsing through high quality document images with DjVu

Abstract: Presents a new image compression technique called "DjVu" that is specifically geared towards the compression of high-resolution, high-quality images of scanned documents in color. With DjVu, any screen connected to the Internet can access and display images of scanned pages while faithfully reproducing the font, color, drawings, pictures and paper texture. A typical magazine page in color at 300 dpi can be compressed down to between 40 to 60 KBytes, approximately 5 to 10 times better than JPEG for a similar level of subjective quality. Black-and-white documents are typically 15 to 30 KBytes at 300 dpi, or 4 to 8 times better than CCITT-G4. A real-time, memory-efficient version of the decoder was implemented, and is available as a plug-in for popular Web browsers.

DjVu: a Compression Method for Distributing Scanned Documents in Color over the Internet.

Abstract: We present a new image compression technique called “DjVu” that is specifically geared towards the compression of scanned documents in color at high revolution. DjVu enable any screen connected to the Internet to access and display images of scanned pages while faithfully reproducing the font, color, drawings, pictures, and paper texture. With DjVu, a typical magazine page in color at 300dpi can be compressed down to between 40 to 60 KB, approximately 5 to 10 times better than JPEG for a similar level of subjective quality. A real-time, memory efficient version of the decoder is available as a plug-in for popular web browsers.