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Business Identification: Spatial Detection. Alexander Darino Weeks 7 & 8 (Abridged). Weaknesses to Current Approach. Business Name Matching. Business Spatial Detection. Latitude Longitude. Geocoding Reverse Geocoding. Nearby Businesses. Business Identification. Image. OCR.

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Business identification spatial detection

Business Identification:Spatial Detection

Alexander Darino

Weeks 7 & 8 (Abridged)


Weaknesses to current approach
Weaknesses to Current Approach

Business Name Matching

Business Spatial

Detection

Latitude

Longitude

Geocoding

Reverse

Geocoding

Nearby Businesses

BusinessIdentification

Image

OCR

Detected Text


Alternative image matching
Alternative: Image Matching


Alternative image matching1
Alternative: Image Matching

  • Weaknesses:

    • Low Availability of Storefront Images (< 50% Avg)

      • George Aiken area businesses with photos: 18/35

      • Brueggers area businesses with photos: 22/40

      • Tambellini area businesses with photos: 8/22

    • Available Images too small (100 x 100)

  • Not a viable solution


Alternative template matching
Alternative: Template Matching

  • Tambellini

  • Tambellini

  • Tambellini

  • Tambellini

  • Tambellini

  • Tambellini

  • Tambellini

  • Tambellini


Alternative template matching1
Alternative: Template Matching

  • SIFT is not a robust solution.

  • Maybe Haar features will work?

  • Moving right along…


Scene text recognition

Moving away from SIFT and revisiting

Scene Text Recognition


Str implementation
STR Implementation

  • STR Implementation: “Automatic Detection and Recognition of Signs From Natural Scenes”

Multiresolution-based potential characters detection

Character/layout geometry and color properties analysis

Refined Detection

Local affine rectification


Multiresolution based potential characters detection
Multiresolution-based potential characters detection

  • Laplacian-of-Guassian Edge Detection

  • Dice image/edges into Patches

    • Combine patches with similar properties into regions

    • Obtain bounding box of region as candidate text

    • Properties include:

      • Mean

      • Variance

      • Intensity(?)


Multiresolution based potential characters detection1
Multiresolution-based potential characters detection


Multiresolution based potential characters detection2
Multiresolution-based potential characters detection

Patches qualify if:


Multiresolution based potential characters detection3
Multiresolution-based potential characters detection


Multiresolution based potential characters detection4
Multiresolution-based potential characters detection


Multiresolution based potential characters detection5
Multiresolution-based potential characters detection



Color properties analysis
Color Properties Analysis

  • Implemented Gaussian Mixture Model (GMM) to obtain μ and σ of foreground/background for: R/G/B/H/I

  • Calculated Confidences that component (RGBHI) can be used to recognize characters

Multiresolution-based potential characters detection

Character/layout geometry and color properties analysis

Refined Detection

Local affine rectification








Evaluation
Evaluation

  • The highest confidence was found in Intensity even though most letters vanish, vs Hue where letters are easily distinguisible

  • This suggests text recognition should occur individually per character

  • The paper further suggests it needs the patches around the individual characters

  • (Woops)


Next steps
Next Steps

  • Goal: Finish STR by next Friday

  • Fix text detector

  • Work with Amir over weekend to implement remaining STR algorithms



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