amit@cs.tamu.edu (Amitabha Mukerjee) (01/29/91)
Archive-name: ai/bibliography/tamu-ai-bib/1991-01-27
Archive-directory: csseq.tamu.edu:/bib/ [128.194.2.20]
Original-posting-by: amit@cs.tamu.edu (Amitabha Mukerjee)
Original-subject: Re: Document Recognition - Information needed
Reposted-by: emv@ox.com (Edward Vielmetti)
Some results of a bibliography search with the keyword
document-recognition. This bibliography is maintained at Texas A&M
University and has about 2000 papers in AI, robotics, and geometric
modeling. You can anonymous ftp it from csseq.tamu.edu (directory
bib).
amit mukerjee
(amit@cs.tamu.edu)
===========================================================================
Antonacci, F., M. Russo, M.T. Pazienza, P.Velardi; 1989
AI::NATURAL-LANGUAGE DOCUMENT-RECOGNITION 2IBM Italy/RomeUniv./Ancona U.
A system for text analysis and lexical knowledge acquisition,
Data and Knowledge Engineering, July 1989, v.4(1):1-20,
Dengel, Andreas; 1989
VISION::AI::DOCUMENT-RECOGNITION RECTANGLE U.Stuttgart-CS
Automatic visual classification of documents,
Proceedings of Intl Workshop on Industrial Applications of Machine
Intelligence and Vision (MIV-89), Tokyo, Japan, April 1988, p.276-281.
{
{ First, align the document by determining the "dominant screw angle"
{ Next, divide up the document into block segments (rectangles). These
{ are then analyzed using a rule-based system. Results show the system
{ to be extremely robust for the class of business letters. -AM 7/89
{ **** Possible project for implementation with the spatial relations
{ algebra.
Ejiri, Masakazu; 1988
IMAGE-PROC::DOCUMENT-RECOGNITION MAP INSPECTION SPATIAL-REASONING RECTANGLE Hitachi CRL,Tokyo
Knowledge-based approaches to practical image processing,
Proceedings of Intl Workshop on Industrial Applications of Machine
Intelligence and Vision (MIV-89), Tokyo, Japan, April 1988, p.1-8.
{
{ Divide the document surface into different rectangular regions (title
{ area, author-name area etc.) using own language FDL (Form Definition
{ Language). Now use this model as input to the vision system - was
{ used to set up system for Japanese birth document. Also some
{ examples of tying maps to views from map locations etc.
Govindraju, Venu, Stephen W. Lam, Debashish Niyogi, David B. Sher, Rohini Srihari, Sargur N. Srihari, and Dacheng Wang; 1989
KBS::VISION DOCUMENT-RECOGNITION NATURAL-LANGUAGE SPATIAL SUNY-Buffalo
Newspaper image understanding,
Knowledge Based Computer Systems, Narosa Publishing House, Bombay, India,
Proceedings of the KBCS '89 conference, Bombay, December 1989,
p.375-384.
{
{ Very powerful paper. First, a block segmentation of the newspaper
{ to determine what part of the paper corresponds to what - news,
{ photo, title, dateline, etc. All are _rectangular blocks_, and
{ this analysis is done without reading any of the contents in the
{ block - based on the characteristics of the document itself. Next,
{ within the appropriate blocks, the characters are recognized using
{ a set of features, such as the strokes, a concavity, a hole, etc.
{
{ The most interesting part is the caption-based picture
{ understanding. Based on a machine parsing of the figure caption
{ and a block segmentation of the image itself, the program labels
{ the portions of the image corresponding to interesting objects.
{ For example, faces are recognized by characteristics of the frontal
{ shape - downwardly converging lines, etc. Sample outputs display
{ the face portions of two persons in an image with a caption-
{ "Wearing their new Celtics sunglasses are Joseph Crowley, standing
{ with the pennant, and seated from the left, Paul Cotter, John Webb
{ and David Buck." This work reported in "Extracting visual
{ information from text: using caption to label human faces in
{ newspaper photographs", in CVPR '89. The reference list points to
{ a bunch of earlier stufdf from Srihari's group. - AM 2/90
Kasturi, Rangachar; Sing T. Bow; Wassim El-Masri; Jayesh Shah; James R. Gattiker; and Umesh B. Mokate; 1990
VISION::RECTANGLE DOCUMENT-RECOGNITION OCR SHAPE 2D SPATIAL-RELATIONS CURVED PennStateU/++
A system for interpretation of line drawings,
IEEE PAMI, v.12(10):978-992
{
{ "An automatic graphics recognition system which can generate a
{ succinct description of various graphical objects and their spatial
{ relationships has many applications." The premise is that artificial
{ images, made up of blocks, text, and geometrical shapes, can be
{ analyzed automatically and symbolic descriptors generated. The first
{ step is to create smallest enclosing rectangules covering intensity
{ changes. Aspect ratios of rectangles are used to identify text vs
{ graphics areas, but this is a blurred area, so histograms do not work
{ very well (**** FUZZY).
{
{ "Collinear component grouping" is performed next (**** tangency and
{ alignment) in the Hough transform domain with multi-scale resolution.
{ A significant part of the effort is in determining which parts of the
{ image are text, and which parts not, with the eventual objective of
{ removing all text portions from the image, leaving only the line
{ drawings. Gradually various parts of the image are removed using
{ "known shape" models such as trapezoid (model based on vertex P, L1,
{ L2, H, theta1, theta2), quasi-hexagon etc.
{
{ Also does flowchart analysis. - AM 12/90
Koons, David B.; 1988
VISION::AI::HYPERMEDIA::DOCUMENT-RECOGNITION SPATIAL-REASONING TAMU-CS
A model for the representation and extraction of visual knowledge from
illustrated texts,
Master's thesis, also Technical report TAMU-88-010, Computer Science
Dept, TAMU, August 1988, 99 pages.
{
{ Relating illustrative diagrams to text portions referring to the
{ diagram; based on a neuroanatomy text with diagrams and text on
{ facing pages. Constructs a dictionary for natural language phrases
{ such as "emerges from", "above", "attaches to"; uses these together
{ with partial models of the objects to construct predicate logic
{ representations; at this stage the figure-analysis was mostly
{ manual. A powerful concept, but one whose time is surely coming.
{ Can apply some of the ideas from [Mukerjee & Joe 89]. -AM 7/89
Srihari, Sargur N.; 1986
VISION::DOCUMENT-RECOGNITION SUNY Buffalo-CS
Document image understanding,
FJCC 1986, p.87-96.
Srihari, Sargur N.; Ching-Huei Wang; Paul W. Palumbo; and Jonathan J. Hull; 1987
AI::VISION::DOCUMENT-RECOGNITION SHAPE RECTANGLE SUNY-Buff
Recognizing address blocks on mail piece: Specialized tools and
problem-solving architecture,
AI Magazine, v.8(4):25-40, Winter 1987.
{
{ Divides up the initial image into 3x3 grid, and identifies the address
{ block area based on a set of five heuristics, which are attenuated
{ through segmentation and thresholding. Some of the rules relate to
{ interpreting block types. e.g.
{
{ Rule MSEGR1:
{ If block A's aspect ration. length, and height and if the number of
{ lines in the block are within the acceptable range for
{ machine-generated address labels, then increase evidence fraction
{ that this is a machine generated destination address label (by .4 for
{ destination address, .3 for return address, and .2 for advertising
{ text).
{
{ Precursor to the much more thorough [Wang and Srihari 89]. - AM 12/90
Wang, Dacheng; and Sargur N. Srihari; 1989
AI::VISION::IMAGE-PROC DOCUMENT-RECOGNITION TEXTURE FILTER RECTANGLE SUNY-Buf
Classification of newspaper image blocks using texture analysis,
Computer Vision Graphics, and Image Processing, v.47:327-352, 1989.
Yashiro, Hiroshi, Tatsuya Murakami, Yoshihiro Shima, Yashiki Nakano, and Hiromichi Fujisawa; 1989
VISION::AI::DOCUMENT-RECOGNITION RECTANGLE Hitachi-CRL,Tokyo
A new method for document structure extraction using generic layout
knowledge,
Proceedings of Intl Workshop on Industrial Applications of Machine
Intelligence and Vision (MIV-89), Tokyo, Japan, April 1988, p.282-287.
{
{ Uses the Form Definition language as in [Ejiri 89] to define document
{ structures.