A Means for Achieving a High Degree of Compaction on Scan-Digitized Printed Text

Ascher, Robert; Nagy, George

doi:10.1109/t-c.1974.223826

Cited by 87 publications

(33 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It worked like a charm! We could not patent it because of a Government anti-monopoly suit, so IBM waited four years before letting us publish it [17]. Our method eventually resurfaced in DjVu and JBIG, but with a critical improvement that we had missed.…”

Section: Prototype Based Text-image Compressionmentioning

confidence: 99%

Estimation, Learning, and Adaptation: Systems That Improve with Use

Nagy¹

2012

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. The accuracy of automated classification (labeling) of single patterns, especially printed, hand-printed, or handwritten characters, has leveled off. Further gains in accuracy require classifying sequences of patterns. Linguistic context, already widely used, relies on 1-D lexical and syntactic constraints. Style-constrained classification exploits the shape-similarity of sets of same-source (isogenous) characters of either the same or different classes. For understanding tables and forms, 2-D structural and relational constraints are necessary. Applications of pattern recognition that do not exceed the limits of human senses and cognition can benefit from green interaction wherein operator corrections are recycled to the classifier.

show abstract

Section: Prototype Based Text-image Compressionmentioning

confidence: 99%

Estimation, Learning, and Adaptation: Systems That Improve with Use

Nagy¹

2012

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…Clustering the connected components (most of which correspond to individual characters) in an isogenous text image is the basis for efficient text-image compression, such as DjVu and JBIG2 [99,100,101]. Clustering of approximate representations of document words was used to reduce the number of comparisons of a query versus document words in the multilingual word indexing scheme mentioned above [102].…”

Section: Supervisedmentioning

confidence: 99%

Adaptive and Interactive Approaches to Document Analysis

Nagy¹,

Veeramachaneni

2008

Machine Learning in Document Analysis and Recognition

View full text Add to dashboard Cite

Summary. This chapter explores three aspects of learning in document analysis: (1) field classification, (2) interactive recognition, and (3) portable and networked applications. Context in document classification conventionally refers to language context, i.e., deterministic or statistical constraints on the sequence of letters in syllables or words, and on the sequence of words in phrases or sentences. We show how to exploit other types of statistical dependence, specifically the dependence between the shape features of several patterns due to the common source of the patterns within a field or a document. This type of dependence leads to field classification, where the features of some patterns may reveal useful information about the features of other patterns from the same source but not necessarily from the same class. We explore the relationship between field classification and the older concepts of unsupervised learning and adaptation. Human interaction is often more effective interspersed with algorithmic processes than only before or after the automated parts of the process. We develop a taxonomy for interaction during training and testing, and show how either human-initiated and machine-initiated interaction can lead to human and machine learning. In a section on new technologies, we discuss how new cameras and displays, web-wide access, interoperability, and essentially unlimited storage provide fertile new approaches to document analysis.

show abstract

“…A number of authors studied the case of binary textual image compression [1] - [6]. Most of the available methods exploit the characteristics of textual images which are different than arbitrary binary images.…”

Section: Textual Image Archiving and Compressionmentioning

confidence: 99%

<title>Wavelet domain textual coding of Ottoman script images</title>

1996

View full text Add to dashboard Cite

Image coding using Wavelet Transform, DCT and similar transform techniques is well established. On the other hand, these coding methods neither take into account the special characteristics of the images in a database nor are they suitable for fast database search. In this paper, the digital archiving of Ottoman printings is considered. Ottoman documents are printed in Arabic letters. In [1], Witten et al. describes a scheme based on finding the characters in binary document images and encoding the positions of the repeated characters. This method efficiently compresses document images and is suitable for database search, but it cannot be applied to Ottoman or Arabic documents as the concept of character is different in Ottoman or Arabic. Typically, one has to deal with compound structures consisting of a group of letters. Therefore, the matching criterion will be according to those compound structures. Furthermore, the text images are gray tone or color images for Ottoman scripts for the reasons that will be described in the paper. In our method the compound structure matching is carried out in wavelet domain which reduces the search space and increases the compression ratio. In addition to the wavelet transformation which corresponds to the linear subband decomposition, we also used nonlinear subband decomposition. The filters in the nonlinear subband decomposition have the property of preserving edges in the low resolution subband image.Keywords: Textual Image Coding, Document Imaging, Image Databases, Wavelet Transforms. TEXTUAL IMAGE ARCHIVING AND COMPRESSIONAn Ottoman document image mainly consists of printed text and some marks and drawings. There can also be gray tone and color images inside the document. Usually, the marks, shady areas and ink smears in the page are important for a historian. As a result, the scanned image should be kept in gray tone or color format for Ottoman archives. the redundancy of these repetitions is the key step in most of the textual image coding algorithms. A good approach to take advantage of this redundancy is to encode the repeated character images and their locations. This method efficiently compresses the textual image and it is appropriate for fast database search. Since the character images are preserved, the keyword search is available via individual characters and their locations.The procedure of textual image coding can be described in a sequence as: 1) Find and extract a mark in the image, 2) add it to the library constructed by these mark images, 3) find the locations of the marks that are similar to the extracted one inside image, and remove those repetitions from the image, 4) go to 1 until all marks in the image are deleted, 5) compress (i) the constructed library and (ii) the symbol locations.This operation is illustrated in Fig. 1. The repetitions of letter "waw" are found. Note that the letter can be connected to other compound structures.SPIE Vol. 2727 / 569A further sixth step in this procedure is proposed in [1] to encode the residue image and p...

show abstract

A Means for Achieving a High Degree of Compaction on Scan-Digitized Printed Text

Cited by 87 publications

References 5 publications

Estimation, Learning, and Adaptation: Systems That Improve with Use

Estimation, Learning, and Adaptation: Systems That Improve with Use

Adaptive and Interactive Approaches to Document Analysis

<title>Wavelet domain textual coding of Ottoman script images</title>

Contact Info

Product

Resources

About