Decoding of Text Lines in Grayscale Document Images

The Document Image Decoding (DID) framework for recognizing printed text in images has been shown in previous work to achieve extremely high recognition accuracy when its models are well matched to the data. To date, DID has been restricted to binary images, in part for computational reasons, and in part because binary scanning is widely available and often of sufficient spatial resolution to make the use of grayscale information unnecessary for reliable recognition. Advances in computer speed and memory, along with the emergence of low-cost digital still cameras and similar devices as alternatives to traditional scanners, motivates the extension of the DID formalism to the lowspatial -resolution grayscale and color domains. To do so requires substantially generalizing DID's image-formation and degradation models. This paper lays out an approach and presents preliminary results on real data

Lossy Compression of Grayscale Document Images by Adaptive-Offset Quantization

This paper describes an adaptive-offset quantization scheme and considers its application to the lossy compression of grayscale document images. The technique involves scalar-quantizing and entropy-coding pixels sequentially, such that the quantizer’s offset is always chosen to minimize the expected number of bits emitted for each pixel, where the expectation is based on the predictive distribution used for entropy coding. To accomplish this, information is fed back from the entropy coder’s statistical modeling unit to the quantizer. This feedback path is absent in traditional compression schemes. Encouraging but preliminary experimental results are presented comparing the technique with JPEG and with fixed-offset quantization on a scanned grayscale text image

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Cluster-Based Probability Model and Its Application to Image and Texture Processing

We develop, analyze, and apply a specific form of mixture modeling for density estimation, within the context of image and texture processing. The technique captures much of the higher-order, nonlinear statistical relationships present among vector elements by combining aspects of kernel estimation and cluster analysis. Experimental results are presented in the following applications: image restoration, image and texture compression, and texture classification. 1 Introduction In many signal processing tasks, uncertainty plays a fundamental role. Examples of such tasks are compression, detection, estimation, classification, and restoration --- in all of these, the future inputs are not known perfectly at the time of system design, but instead must be characterized only in terms of their "typical," or "likely" behavior, by means of some probabilistic model. Every such system has a probabilistic model, be it explicit or implicit. Often, the level of performance achieved by such a syste..

Adding Linguistic Constraints to Document Image Decoding: Comparing the Iterated Complete Path and Stack Algorithms

Conference paperBeginning with an observed document image and a model of how the image has been degraded, Document Image Decoding recognizes printed text by attempting to find a most probable path through a hypothesized Markov source. The incorporation of linguistic constraints, which are expressed by a sequential predictive probabilistic language model, can improve recognition accuracy significantly in the case of moderately to severely corrupted documents. Two methods of incorporating linguistic constraints in the best-path search are described, analyzed and compared. The first, called the iterated complete path algorithm, involves iteratively rescoring complete paths using conditional language model probability distributions of increasing order, expanding state only as necessary with each iteration. A property of this approach is that it results in a solution that is exactly optimal with respect to the specified source, degradation, and language models; no approximation is necessary. The second approach considered is the Stack algorithm, which is often used in speech recognition and in the decoding of convolutional codes. Experimental results are presented in which text line images that have been corrupted in a known way are recognized using both the ICP and Stack algorithms. This controlled experimental setting preserves many of the essential features and challenges of real text line decoding, while highlighting the important algorithmic issues