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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 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…
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(?)
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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