Update README.md

README.md

@@ -1,12 +1,9 @@
 ---
 license: cc-by-4.0
-
 task_categories:
 - tabular-classification
-
 language:
 - en
-
 tags:
 - tabular
 - timeseries
@@ -20,10 +17,125 @@ tags:
 - esp32-s3
 - sri-lanka
 - sri-lankan-rupee
-- physical-banknotes
-
 pretty_name: "SLR-NoteSense: Dual-Sensor RGBC Sri Lankan Banknote Dataset"
-
 size_categories:
 - 10K - 100K
----
+---
+
+# SLR-NoteSense
+
+SLR-NoteSense is a dual-sensor RGBC dataset for Sri Lankan banknote denomination recognition.
+
+The dataset contains 12,668 paired sensor measurements collected from 634 distinct physical Sri Lankan banknotes across six denominations:
+
+- LKR 20
+- LKR 50
+- LKR 100
+- LKR 500
+- LKR 1000
+- LKR 5000
+
+## Dataset Details
+
+Two TCS34725 color sensors were used to collect red, green, blue, and clear-channel measurements.
+
+Sensor settings:
+
+- Integration time: 50 ms
+- Gain: 4X
+- Number of sensors: 2
+- Number of physical banknotes: 634
+- Raw measurements: 12,668
+- Stable measurements after preprocessing: 12,035
+
+## Dataset Structure
+
+Each row contains:
+
+- label
+- note_id
+- scan_id
+- sample_index
+- s1_r
+- s1_g
+- s1_b
+- s1_clear
+- s1_nr
+- s1_ng
+- s1_nb
+- s2_r
+- s2_g
+- s2_b
+- s2_clear
+- s2_nr
+- s2_ng
+- s2_nb
+
+Normalized channels are calculated as:
+
+R/C, G/C, and B/C.
+
+## Physical Banknote Distribution
+
+| Denomination | Physical Banknotes |
+|---|---:|
+| LKR 20 | 102 |
+| LKR 50 | 102 |
+| LKR 100 | 106 |
+| LKR 500 | 111 |
+| LKR 1000 | 106 |
+| LKR 5000 | 107 |
+| Total | 634 |
+
+## Preprocessing
+
+The first sensor reading of each acquisition sequence showed a sensor-settling transient and was excluded from the stable dataset.
+
+A total of 60 missing denomination labels were restored from denomination-specific source files.
+
+For machine-learning evaluation, all measurements from the same physical banknote should remain in the same train, validation, or test partition.
+
+Use denomination + note_id as the physical-banknote group identifier.
+
+## Baseline Validation
+
+An RBF Support Vector Machine using eight scan-level features achieved:
+
+- Group-aware repeated cross-validation accuracy: 98.33%
+- Macro F1-score: 98.33%
+- Group-held-out accuracy: 96.85%
+- Python-to-C++ prediction agreement: 634/634
+
+These results are provided as technical validation of the dataset.
+
+## Intended Uses
+
+This dataset is intended for research in:
+
+- banknote denomination recognition
+- embedded machine learning
+- color-sensor classification
+- assistive technology
+- feature engineering
+- group-aware machine-learning evaluation
+
+## Out-of-Scope Uses
+
+The dataset is not intended for:
+
+- counterfeit detection
+- banknote authentication
+- financial security verification
+- banknote valuation
+
+## License
+
+This dataset is released under the Creative Commons Attribution 4.0 International license.
+
+## Citation
+
+Citation information will be added after publication of the associated data paper.
+
+## Dataset Version
+
+Version 1.0