Jaekyu Ha

Recursive X-Y cut using bounding boxes of connected components

A top-down page segmentation technique known as the recursive X-Y cut decomposes a document image recursively into a set of rectangular blocks. This paper proposes that the recursive X-Y cut be implemented using bounding boxes of connected components of black pixels instead of using image pixels. The advantage is that great improvement can be achieved in computation. In fact, once bounding boxes of connected components are obtained, the recursive X-Y cut is completed within an order of a second on Sparc-10 workstations for letter-sized document images scanned at 900 dpi resolution.

Document layout structure extraction using bounding boxes of different entitles

The paper presents an efficient technique for document page layout structure extraction and classification by analyzing the spatial configuration of the bounding boxes of different entities on the given image. The algorithm segments an image into a list of homogeneous zones. The classification algorithm labels each zone as test, table, line-drawing, halftone, ruling, or noise. The text lines and words are extracted within text zones and neighboring text lines are merged to form text blocks. The tabular structure is further decomposed into row and column items. Finally, the document layout hierarchy is produced from these extracted entities.

Document page decomposition by the bounding-box project

This paper describes a method for extracting words, textlines and text blocks by analyzing the spatial configuration of bounding boxes of connected component on a given document image. The basic idea is that connected components of black pixels can be used as computational units in document image analysis. In this paper, the problem of extracting words, textlines and text blocks is viewed as a clustering problem in the 2-dimensional discrete domain. Our main strategy is that profiling analysis is utilized to measure horizontal or vertical gaps of (groups of) components during the process of image segmentation. For this purpose, we compute the smallest rectangular box, called the bounding box, which circumscribes a connected component. Those boxes are projected horizontally and/or vertically, and local and global projection profiles are analyzed for word, textline and text-block segmentation. In the last step of segmentation, the document decomposition hierarchy is produced from these segmented objects.

Zone classification in a document using the method of feature vector generation

A document can be divided into zones on the basis of its content. For example, a zone can be either text or non-text. This paper describes an algorithm to classify each given document zone into one of nine different classes. Features for each zone such as run length mean and variance, spatial mean and variance, fraction of the total number of black pixels in the zone, and the zone width ratio for each zone are extracted. Run length related features are computed along four different canonical directions. A decision tree classifier is used to assign a zone class on the basis of its feature vector. The performance on an independent test set was 97%.

Document page decomposition using bounding boxes of connected components of black pixels

Segmentation of document images can be performed by projecting image pixels. This pixel projection approach is one of widely used top-down segmentation methods and is based on the assumption that the document image has been correctly deskewed. Unfortunately, the pixel projection approach is computationally inefficient. It is because each symbol is not treated as a computational unit. In this paper, we explain a new technique which is highly tactical in the profiling analysis. Instead of projecting image pixels, we first compute the bounding box of each connected component in a document image and then we project those bounding boxes. Using the new technique, this paper describes how to extract words, text lines, and text blocks (e.g., paragraphs). This bounding box projection approach has many advantages over the pixel projection approach. It is less computationally involved. When applied to text zones, it is also possible to infer from the projection profiles how bounding boxes (and, therefore, primitive symbols) are aligned and/or where significant horizontal and vertical gaps are present. Since the new technique manipulates only bounding boxes, it can be applied to any noncursive language documents.

The implementation methodology for a CD-ROM English document database

Producing a database of scanned document images for development or evaluation of OCR and document image understanding algorithms is neither easy nor inexpensive. The authors first briefly describe the makeup of a database of scanned document images of scientific and technical documents written in English which are being produced in a CD-ROM format. Then, the authors concentrate on the implementation methodology used to prepare the database. The methodology gives the protocols for each step of the database preparation, and the error model used for the estimation of the ground-truth errors that may exist in the database is discussed.<<ETX>>

Document zone classification using sizes of connected components

In this paper, we describe a feature based supervised zone classifier using only the knowledge of the widths and the heights of the connected-components within a given zone. The distribution of the widths and the heights of the connected-components is encoded into a n multiplied by m dimensional vector in the decision making. Thus, the computational complexity is in the order of the number of connected-components within the given zone. A binary decision tree is used to assign a zone class on the basis of its feature vector. The training and testing data sets for the algorithm are drawn from the scientific document pages in the UW-I database. The classifier is able to classify each given scientific and technical document zone into one of the eight labels: text of font size 8-12, text of font size 13-18, text of font size 19-36, display math, table, halftone, line drawing, and ruling, in real time. The classifier is able to discriminate text from non-text with an accuracy greater than 97%.

Implementation methodology and error analysis for the University of Washington English Document Image Database-I

Document database preparation is a very time-consuming job and usually requires the involvement of many people. Any database is prone to having errors however carefully it was constructed. To assure the high quality of the document image database, a carefully planned implementation methodology is absolutely needed. In this paper, an implementation methodology that we employed to produce the UW English Document Image Database I is described. The paper also discusses how to estimate the distribution of errors contained in a database based on a double-data entry/double verification procedure.

Estimation of the position and orientation of a planar surface using multiple beams

A sensory system consisting of a camera and several laser beams is described. It is designed for estimating the parameters of a planar surface with respect to the camera. Estimation is possible when the beam positions and directions are known, as well as the image of the beam spots on a planar surface. The system is readily applicable in an industrial environment for automation, if it is attached to a robot arm and is connected to a computer.<<ETX>>