Many companies still operate critical business processes using paper-based forms, including customer surveys, inspections, contracts and invoices. Converting those handwritten forms to symbolic data is expensive and complicated. This paper presents an overview of the Image-Based Document Management (IBDM) system for analyzing handwritten forms without requiring conversion to symbolic data. Strokes captured in a questionnaire on a tablet are separated into fields that are then displayed in a spreadsheet. Rows represent documents while columns represent corresponding fields across all documents. IBDM allows a process owner to capture and analyze large collections of documents with minimal IT support. IBDM supports the creation of filters and queries on the data. IBDM also allows the user to request symbolic conversion of individual columns of data and permits the user to create custom views by reordering and sorting the columns. In other words, IBDM provides a "writing on paper" experience for the data collector and a web-based database experience for the analyst.

Shared office information appliances such as copiers, scanners and meeting-room displays are increasingly being integrated into complex electronic document workflows. To support this new role these appliances are being designed with advanced features such as optical character recognition, networked storage, content-based routing and integration with back-end databases, yet availability of these features is often hampered by various factors. Here we present an architecture, code named Odessa, that moves all task-specific processing off the office appliance and on to a user's own device: PC / laptop, or smartphone. Key features of the architecture include a simple RESTful HTTP application interface and automatic service discovery.

This poster presents an implementation of the JPIP-JPM standard which allows low bandwidth access to remote high resolution documents. The implementation goes beyond the standard by allowing simple editing operations at the client without obtaining all of the compressed data. These edits form a new version of the document that can be easily uploaded over low bandwidth connections.

For practical image compression systems, handling images divided into independent tiles in the pixel domain is important. However, tiling introduces artifacts when transform coefficients are quantized The paper presents a "detiling" solution applicable to wavelet transform coefficients. The solution, called projection onto scaling functions, eliminates the blocking artifacts by computing approximations that match the smoothness of the compressed image inside a tile. This solution is applicable to any wavelet filter - extending previous work that applied only to a limited class of wavelet filters - and specifically targets decoding of JPEG2000 compressed images.

The problem of image enhancement arises in many applications such as scanners, copiers and digital cameras. Enhancement often includes a denoising and a deblurring or sharpening step. Similar to image compression, state-of-the-art denoising techniques use wavelet bases instead of Fourier bases since wavelet domain processing provides local adaptation in smooth and non-smooth parts due to the theoretical link between wavelets and smoothness spaces. In this paper the same smoothness spaces are used to propose a way of performing sharpening and smoothing of signals with wavelets (WSS) in Besov spaces. As an application the completely wavelet-based enhancement of a scanned document is discussed.

As the applications of digital imagery expand in resolution and pixel fidelity there is a greater need for more efficient compression and extraction of images and sub-images. No longer is it sufficient to compress and decompress an image for a specific target device. The ability to handle many types of image data, extract images at different resolutions and quality, zoom and pan, and to extract regions-of-interest are the new measures of image compression systems. CREW (compression with reversible embedded wavelets) is a high quality image compression system that is progressive from high compression to lossless, pyramidal, and supports regions-of-interest and multiple image types. This paper describes the CREW system and format, shows how the correct data can be quickly extracted from a CREW file to support a variety of target devices, describes the mechanisms needed for panning, zooming, and fixed-size compression, and explains the superior performance on bi-level and graphic images.

Compression with reversible embedded wavelets (CREW) consists of three major parts: a wavelet transform, an embedded context model, and a binary entropy coder. The wavelet transform is a special implementation of high quality wavelet which provides perfect reconstruction with finite precision coefficients. The context model describes the wavelet coefficients in a particular order with conditional probability estimates. Finally the FSM-coder, a binary entropy coder with functionality similar to the QM-coder, is used to generate the final code stream.

Compression with reversible embedded wavelets (CREW) is a unified lossless and lossy continuous-tone still image compression system. Wavelet coefficients are encoded in a bit-significance embedded order, allowing lossy compression by truncating the compressed data after encoding. CREW's context-based coding, called Horizon coding, takes advantage of the spatial and spectral information available in the wavelet domain and adapts well to the lessor significant bits. CREW's capabilities have been enhanced to include a binary mode for compression of binary and graphic images. This also improves compression on some images that do not use the full dynamic range. The binary mode encodes bit-planes of the image without using the transform. This JBIG-like system uses the resources already available in the CREW system. This paper introduces some basic concepts of CREW and describes the binary mode in detail.

Compression with Reversible Embedded Wavelets (CREW) is a unified lossless and lossy continuous tone still image compression system. It is wavelet based using a "reversible" approximation of one of the best wavelet filters. Reversible wavelets are linear filters with non linear rounding which implement exact reconstruction systems with minimal precision integer arithmetic. Wavelet coefficients are encoded in a bit significance embedded order, allowing lossy compression by simply truncating the compressed data. For coding of coefficients, CREW uses a method similar to J. Shapiro's (1993) zero tree, and a completely novel method called Horizon. Horizon coding is a context based coding that takes advantage of the spatial and spectral information available in the wavelet domain. CREW provides state of the art lossless compression of medical images (greater than 8 bits deep), and lossy and lossless compression of 8 bit deep images with a single system. CREW has reasonable software and hardware implementations.

Efforts to build high-speed hardware for many different entropy coders are limited by fundamental feedback loops. A method that allows for parallel compression in hardware is described. This parallelism results in extremely high rates, 100 million symbols/second or higher. The system is generalizable to any lossless or lossy system with deterministic decompression. Prototype hardware that divides the data into multiple streams that feed parallel coders is presented. The problem of efficient transmission of multiple streams of variable-length coded data is solved by a unique coded data interleave method.>