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Spatial Business Detection and Recognition from Images

In Week 12 of our project on spatial business detection and recognition, we accomplished critical enhancements to the STR code. We fixed several bugs and transitioned from per-patch LDA to per-character SVM, resulting in a significantly longer training time but slightly faster classification. The accuracy remains comparable and possibly improved, without requiring extensive preprocessing eroding or dilating. Our goals for today include completing the report, updating the website, and organizing archived code. Next week, we aim to implement an improved textual word-matching algorithm and enhance visual word recognition capabilities.

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Spatial Business Detection and Recognition from Images

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  1. Spatial Business Detection and Recognition from Images Alexander Darino Week 12

  2. Major Accomplishments • Fixed several bugs in STR code • Converted STR code from per-patch LDA to per-character SVM.Preliminary Results: • Very long training time, slightly faster classification • Accuracy is comparable, possibly better • Does not seem to require erosion/dilation during preprocessing, but scaling still appears necessary

  3. Today’s Goals • Finish the Report • Update website • Organize code for archiving • Filter out votes from incorrect inversion • Test out STR on several real-world examples

  4. Goals for Next Week • Implement proposed Levenshtein distance-minization textual word-matching algorithm • Significant improvement over our prior work • Visual word matching: Use existing STR system to recognize entire words/logos • Incorporate aspect ratio of longest anticipated word (from the names of proximate businesses)

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