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Graphics Recognition – from Re-engineering to RetrievalPowerPoint Presentation

Graphics Recognition – from Re-engineering to Retrieval

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Document Analysis in the IR era

- Information is at the core of industrial strategies
- A lot of digital or digitized information, but often in very “poor” formats
- The challenge: not necessarily re-engineering of documents, but enrich poorly structured information, add (limited) amount of semantics, build indexes
- Purposes: browsing, navigation, indexing
- DAR methods and tools useful, but must be adapted

Specific challenges of large-scale IR applications

- Genericity: we cannot necessarily build a complete and exhaustive a priori model of contextual knowledge (ontology)
- Adaptability: various input data – scanned paper, PDF, DXF, HTML, GIF… – various resolutions
- Robustness: “back-office” applications
- Efficiency: online searching in heterogeneous data
- Scaling: methods have to scale to increasing number of symbols/features

DAR and IR

- Media without (or with very little) contextual knowledge
- Image-based indexing and retrieval, indexing of video sequences
- Documents do explicitly convey information from one person to another person
- Much more structure, syntax and semantics

DAR and IR – some examples

- Indexing and/or searching scanned text without OCR
- Similarities, signatures
- Query or index on layout structure
- Table spotting
- Keyword spotting
- …

What about Graphics Recognition?

- Subfield of DAR, for graphics-rich documents
- Numerous methods for various analysis and recognition problems
- Raster-to-vector conversion
- Text/graphics separation
- Symbol recognition

- Many specific technical areas: maps, architectural drawings, engineering drawings, diagrams and schematics, …

Graphics recognition methods

- Text/graphics separation

Graphics recognition methods

- Vectorization

Graphics recognition and IR applications

- Usual text-based indexing and retrieval still useful
- But need for access to other kinds of information:
- Symbols
- Text-drawing connections
- Description-illustration connections

Some contributions

- Syeda-Mahmood – maintenance drawings

IEEE Trans. On PAMI 21(8):737-751, Aug. 1999

Some contributions

- Arias et al., Najman et al. – use of information contained in legend / title block

Proc. GREC’01, Kingston (Ontario, Canada), p.19-26, Sept. 2001

Some contributions

- Samet & Soffer – symbols from legend

IEEE Trans. On PAMI 18(8):783-798, Aug. 1996

Some contributions

- Müller & Rigoll – graphical retrieval in database of engineering drawings

Proc. ICDAR’99, Bangalore (India), pp. 697-700, Sept. 1999

Some contributions

- Boose et al. (Boeing) – Generation of Layered Illustrated Parts Drawings (GREC’ 03)

Proc. GREC’03, Barcelona, pp. 139-144

Symbol recognition

Before we move on:

1st contest on

symbol recognition

held last week

See IAPR TC10 homepage

for further details

- Natural features for indexing and retrieval
- Most methods work with known databases of reference symbols – what about interactive querying of arbitrary symbols?
- From segmentation followed by recognition, to segmentation-free recognition, or segmenting while recognizing
- Scalability
- Efficiency / complexity
- Discrimination power

- Signatures

Image-based signatures

- Compute invariant signatures on binary document image
- F-signatures (ICDAR’01)
- Radon transform: R-signatures [Tabbone & Wendling]
- Ridgelets [Ramos Terrades & Valveny – GREC’03] – aka wavelet transform of Radon transform

R-signatures

Detection of arrowheads [Girardeau & Tabbone]

DEA degree thesis, INPL, Nancy, Jul. 2002

R-signatures

Another example [Girardeau & Tabbone]

Vector-based signatures

[Dosch & Lladós – GREC’03]

- Based on set of basic graphical features:
- Parallelism
- Overlap
- Collinearity
- T- and V-junctions

- Quality factor associated with the various relations
- Match signatures of reference symbols with signatures of buckets

Towards symbol spotting

- Pre-compute – or compute on the spot – a set of basic signatures
- Can be sufficient for symbol spotting and retrieval
- Followed by classical symbol recognition if more discrimination is needed

Symbol spotting

- [Jabari & Tabbone] : graph matching through probabilistic relaxation, with nodes=segments and vertices=relations

DEA degree thesis, INPL, Nancy, Jul. 2003

Symbol spotting

- [Jabari & Tabbone] : another example

Combining Text and Graphics

- Extracting Text/Graphics relationships within document
- Using Text matching for inter-document relationships
- Transitive inter-document Graphics matching
- No need for complex graphics matching
- Restricted to well known document types

Example: continuation of Wiring Diagrams (Boeing)

- [Baum et al. – GREC’03]

Proc. GREC’03, Barcelona, pp. 132-138

Scan2XML Example

Proc. GREC’01, Kingston (Ontario, Canada), pp. 312-325

Indexing and Semantics

- Signature + metric
- Semantics = measured distance to signature
- Applies only to homogenous contexts
- Pre-segmented images
- Pre-determined image classes
- Implicit application of domain kowledge
- ...

- Semantics = Syntax

Example

Signature type A

Metric M

Signature value l

Semantics1 = (1, 1)

Semantics2 = (2, 2)

M(l,s1) < m1 ?

M(l,s2) < m2 ?

semantics = measurement to reference value

Heterogenous Document Bases

- Semantics do not have a unique syntax anymore
- Syntax metrics may be context sensitive
- Semantics = Syntax + Context
Context needs to be considered

Example

Context 1:

Signature type A

Metric M

Context 2:

Signature type B

Metric N

Signature value l

What if

M(l,s1) < m1and

N(l,t2) < n2 ?

(1, 1) = Semantics1 = (t1, n1)

(2, 2) = Semantics2 = (t2, n2)

Data

Data

(syntax)

(semantics)

(semantics)

A step to taking into account context(while consolidating existing approaches)

Component Algebra :

- Image Analysis = Pipeline
- Syntax + algorithm = semantics

Algorithm

Algorithm

Syntax and semantics need not be distinguished

Component Algebra

- Components :
Known and implemented document analysis algorithms, taking input data from one domain, and producing data into another domain.

- Application Context :
Set of all available Components.

- Semantics :
Data sets needed by or produced by Components.

Advantages

- Each node is a semantic concept, semantic relationships are explicitly expressed.
- Structure may support automatic reasoning and knowledge inference.
- Context is embedded in components, different contexts give different paths in the graph.
- Highly scalable and open architecture.
- Bridge between signal-level document analysis and high-level document representation.

However ...

The formalism exists, the realization doesn't (yet)

- What about parametrization ?
- How context independant can you get ?
- What about « guessing » context appropriateness ?
- How to design fully interoperable components ?

Conclusion

- A lot of DA methods – and more specifically GR methods – can be of direct use in IR, indexing and browsing applications
- Specific challenges
- Scaling and efficiency
- Heterogeneous sets of documents
- Incomplete domain knowledge
- Symbol spotting
- On-the-fly symbol searching

- Sketch of open framework for including document semantics when context can be heterogeneous

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