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

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. 1