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Neural networks are a powerful technology forclassification of visual inputs arising from documents.However, there is a confusing plethora of different neuralnetwork methods that are used in the literature and inindustry. This paper describes a set of concrete bestpractices that document analysis researchers can use toget good results with neural networks. The mostimportant practice is getting a training set as large aspossible: we expand the training set by adding a newform of distorted data. The next most important practiceis that convolutional neural networks are better suited forvisual document tasks than fully connected networks. Wepropose that a simple "do-it-yourself" implementation ofconvolution with a flexible architecture is suitable formany visual document problems. This simpleconvolutional neural network does not require complexmethods, such as momentum, weight decay, structure-dependentlearning rates, averaging layers, tangent prop,or even finely-tuning the architecture. The end result is avery simple yet general architecture which can yieldstate-of-the-art performance for document analysis. Weillustrate our claims on the MNIST set of English digitimages.

This paper presents an integrated approach to parsing textual structure in freeform handwritten notes. Text-graphics classification and text layout analysis are classical problems in printed document analysis, but the irregularity in handwriting and content in freeform notes reveals limitations in existing approaches. We advocate an integrated technique that solves the layout analysis and classification problems simultaneously: the problems are so tightly coupled that it is not possible to solve one without the other for real user notes. We tune and evaluate our approach on a large corpus of unscripted user files and reflect on the difficult recognition scenarios that we have encountered in practice.

How do you tell a computer from a human? The situation arises often on the Internet, when online polls are conducted, accounts are requested, undesired email is received, and chat-rooms are spammed. The approach we use is to create a visual challenge that is easy for humans but difficult for a computer. More specifically, our challenge is to recognize a string of random distorted characters. To pass the challenge, the subject must type in the correct corresponding ASCII string. From an OCR point of view, this problem is interesting because our goal is to use the vast amount of accumulated knowledge to defeat the state of the art OCR algorithms. This is a role reversal from traditional OCR research. Unlike many other systems, our algorithm is based on the assumption that segmentation is much more difficult than recognition. Our image challenges present hard segmentation problems that humans are particularly apt at solving. The technology is currently being used in MSN's Hotmail registration system, where it has significantly reduced daily registration rate with minimal Consumer Support impact.

Many image coders map the input image to a transform domain, and encode coefficients in that domain. Usually, encoders use previously transmitted samples to help estimate the probabilities for the next sample to be encoded. This backward probability estimation provides a better PDF estimation, without need to send any side information. A new method of encoding was proposed that goes one step further: besides past samples, it also uses information about the current sample in computing the PDF for the current sample. Yet, no side information was transmitted. The initial PDF estimate is based on a Tarp filter, but probabilities are then progressively refined for non-zero samples. Results are superior to JPEG2000, and to bit-plane Tarp.

On-line adaptation to nonstationary distributions is essential to good performance in image coding. Fixed-size contexts (with adaptive tables) are also widely used, in conjunction with arithmetic encoders, in state-of-the-art codecs. In contrast, we propose a simple two-dimensional filter that directly outputs the probability distribution function (PDF) estimate necessary to drive an adaptive arithmetic encoder. The filter is isotropic, in the sense that the impact of a previously encoded bit depends only on its value and distance to the bit to be coded. Surprisingly, this simple filter yields results comparable to or better than JPEG2000. It also brings an interesting distinction between on-line and off-line learning, and their relative importance in compression.

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.

This paper presents an algorithm for the compression of images that have "don't care" regions within them. Such regions are specified by an additional binary image, the mask, which we assume is available at both the coder and the encoder (it is transmitted separately and without loss). Unlike previous approaches, which typically fill in the masked pixels with values geared toward good compression of the image, we use a special wavelet transform which is a function of the mask, but which can be computed efficiently. We compare our method with the previous approaches by measuring the compression for a given PSNR on the valid pixels. Our algorithm is both faster and yields compression improvements of up to 18% over the current state-of-the-art coders.