DOI: 10.7910/dvn/uot0re
title: SbNet Signboard Detection and Classification Dataset

description: &lt;h2&gt;Phase 1: Signboard Detection Dataset&lt;/h2&gt;
&lt;i&gt;This phase focuses on detecting signboards in street images.&lt;/i&gt;&lt;br&gt;

- **Total Images:** 8,366&lt;br&gt;
- **Image Format:** JPG (8,366 images)&lt;br&gt;
- **Resolution:**&lt;br&gt;
 - Minimum: (720, 443)&lt;br&gt;
 - Maximum: (9,280, 8,285)&lt;br&gt;
 - Mean: (4,202, 3,138)&lt;br&gt;
 - Median: (4,032, 3,024)&lt;br&gt;
- **Aspect Ratio:**&lt;br&gt;
 - Minimum: 0.5625&lt;br&gt;
 - Maximum: 5.7043&lt;br&gt;
 - Mean: 1.3691&lt;br&gt;
 - Most Frequent: 1.3333&lt;br&gt;
 - Standard Deviation: 0.2329&lt;br&gt;
- **File Size (KB):**&lt;br&gt;
 - Minimum: 88.19 KB&lt;br&gt;
 - Maximum: 41,266.50 KB&lt;br&gt;
 - Mean: 5,796.19 KB&lt;br&gt;
 - Total Dataset Size: 48,490,924.91 KB&lt;br&gt;
- **Color Statistics:**&lt;br&gt;
 - Color Mode: RGB (8,366 images)&lt;br&gt;
 - Mean Color (RGB): (110.32, 112.77, 118.16)&lt;br&gt;
 - Standard Deviation (RGB): (65.71, 65.36, 65.82)&lt;br&gt;
- **Brightness:**&lt;br&gt;
 - Average: 114.10&lt;br&gt;

---&lt;br&gt;

&lt;h2&gt;Phase 2: Region of Text Interest (RTI) Detection Dataset&lt;/h2&gt;
&lt;i&gt;This phase focuses on detecting specific text regions (names and addresses) within signboards.&lt;/i&gt;&lt;br&gt;

- **Total Images:** 8,036&lt;br&gt;
- **Image Format:** JPG (8,036 images)&lt;br&gt;
- **Resolution:**&lt;br&gt;
 - Minimum: (552, 156)&lt;br&gt;
 - Maximum: (9,228, 4,682)&lt;br&gt;
 - Mean: (2,753, 808)&lt;br&gt;
 - Median: (2,741, 781)&lt;br&gt;
- **Aspect Ratio:**&lt;br&gt;
 - Minimum: 0.9615&lt;br&gt;
 - Maximum: 11.3835&lt;br&gt;
 - Mean: 3.6058&lt;br&gt;
 - Most Frequent: 4.0&lt;br&gt;
 - Standard Deviation: 1.2475&lt;br&gt;
- **File Size (KB):**&lt;br&gt;
 - Minimum: 40.54 KB&lt;br&gt;
 - Maximum: 7,968.94 KB&lt;br&gt;
 - Mean: 653.67 KB&lt;br&gt;
 - Total Dataset Size: 5,252,868.26 KB&lt;br&gt;
- **Color Statistics:**&lt;br&gt;
 - Color Mode: RGB (8,036 images)&lt;br&gt;
 - Mean Color (RGB): (137.58, 136.29, 144.00)&lt;br&gt;
 - Standard Deviation (RGB): (47.26, 49.73, 50.89)&lt;br&gt;
- **Brightness:**&lt;br&gt;
 - Average: 138.74&lt;br&gt;

---&lt;br&gt;
&lt;br&gt;
&lt;h2&gt;Named Entity Recognition (NER) Dataset&lt;/h2&gt;
&lt;i&gt;This dataset is used for categorizing extracted text from signboards.&lt;/i&gt;&lt;br&gt;
&lt;br&gt;
- **Total Entries:** 42,547&lt;br&gt;
- **Unique Categories:** 10&lt;br&gt;
- **Category Distribution:**&lt;br&gt;
 - Religious Sites: 10,641&lt;br&gt;
 - Retail Outlets: 8,275&lt;br&gt;
 - Educational Institutions: 6,826&lt;br&gt;
 - Healthcare Institutions: 4,708&lt;br&gt;
 - Restaurants: 3,868&lt;br&gt;
 - Pharmacies: 3,637&lt;br&gt;
 - Parks: 1,547&lt;br&gt;
 - Banks: 1,121&lt;br&gt;
 - Stations: 1,094&lt;br&gt;
 - Hotels: 830&lt;br&gt;
&lt;br&gt;
#### **Word Count Statistics:**&lt;br&gt;
- **Overall Word Count:**&lt;br&gt;
 - Maximum: 18&lt;br&gt;
 - Minimum: 1&lt;br&gt;
 - Mean: 3.82&lt;br&gt;
- **Category-Wise Word Count:*&lt;br&gt;*
 - **Banks:** Mean: 4.65, Max: 11, Min: 1&lt;br&gt;
 - **Educational Institutions:** Mean: 4.60, Max: 18, Min: 1&lt;br&gt;
 - **Healthcare Institutions:** Mean: 4.02, Max: 16, Min: 1&lt;br&gt;
 - **Religious Sites:** Mean: 4.36, Max: 17, Min: 1&lt;br&gt;
 - **Retail Outlets:** Mean: 3.08, Max: 15, Min: 1&lt;br&gt;
 - **Restaurants:** Mean: 3.36, Max: 13, Min: 1&lt;br&gt;
 - **Pharmacies:** Mean: 2.91, Max: 13, Min: 1&lt;br&gt;
 - **Parks:** Mean: 3.10, Max: 11, Min: 1&lt;br&gt;
 - **Stations:** Mean: 3.72, Max: 17, Min: 1&lt;br&gt;
 - **Hotels:** Mean: 3.12, Max: 12, Min: 1&lt;br&gt;

This dataset is structured for a two-phase object detection pipeline with an additional text classification task to categorize extracted text from detected regions.