Update README.md

README.md

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 # SLR-NoteSense
 
-SLR-NoteSense is a dual-sensor RGBC dataset for Sri Lankan banknote denomination recognition.
+SLR-NoteSense is a dual-sensor red-green-blue-clear (RGBC) dataset for Sri Lankan banknote denomination recognition. The seven-denomination update adds the LKR 2000 class to the original six-denomination dataset.
 
-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
+The dataset contains **14,846 paired acquisition rows** from **743 distinct physical banknotes**, covering LKR **20, 50, 100, 500, 1000, 2000, and 5000**. After applying the documented sensor-settling rule, the stable view contains **14,104 paired measurement rows**.
 
 ## Dataset Details
 
-Two TCS34725 color sensors were used to collect red, green, blue, and clear-channel measurements.
-
-Sensor settings:
+Two TCS34725 color sensors connected to an ESP32-S3 collected red, green, blue, and clear-channel measurements.
 
-- 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
+| Property | Value |
+|---|---:|
+| Sensors | 2 × TCS34725 |
+| Integration time | 50 ms |
+| Gain | 4× |
+| Denominations | 7 |
+| Distinct physical banknotes | 743 |
+| Raw paired measurements | 14,846 |
+| Stable paired measurements | 14,104 |
+| Derived note-level samples | 743 |
 
 ## 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.
+Each raw measurement row contains the following fields:
+
+- `label`: Banknote denomination in LKR; missing in some original source rows.
+- `note_id`: Physical-banknote identifier within its denomination.
+- `scan_id`: Acquisition identifier.
+- `sample_index`: Ordered position within an acquisition sequence.
+- `s1_r`, `s1_g`, `s1_b`, `s1_clear`: Sensor 1 raw RGBC measurements.
+- `s1_nr`, `s1_ng`, `s1_nb`: Sensor 1 R/C, G/C, and B/C ratios.
+- `s2_r`, `s2_g`, `s2_b`, `s2_clear`: Sensor 2 raw RGBC measurements.
+- `s2_nr`, `s2_ng`, `s2_nb`: Sensor 2 R/C, G/C, and B/C ratios.
+
+For each sensor, normalized channels are calculated as `R/C`, `G/C`, and `B/C`, where `C` is the clear-channel reading. Each row contains a paired reading from both sensors. Use **(`label`, `note_id`)** as the globally unique physical-banknote key because `note_id` restarts within each denomination.
 
 ## 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 |
+| Denomination (LKR) | Physical banknotes | Raw rows | Stable rows |
+|---|---:|---:|---:|
+| 20 | 102 | 2,038 | 1,936 |
+| 50 | 102 | 2,038 | 1,936 |
+| 100 | 106 | 2,118 | 2,013 |
+| 500 | 111 | 2,218 | 2,107 |
+| 1000 | 106 | 2,118 | 2,012 |
+| 2000 | 109 | 2,178 | 2,069 |
+| 5000 | 107 | 2,138 | 2,031 |
+| **Total** | **743** | **14,846** | **14,104** |
 
-## Preprocessing
+The new `2000.csv` contributes 109 distinct LKR 2000 notes. An older eight-note LKR 2000 file is **not included** because its note identifiers overlap with those in the newer file and the physical-note identities are unverified. Do not merge the two files without resolving this overlap.
 
-The first sensor reading of each acquisition sequence showed a sensor-settling transient and was excluded from the stable dataset.
+## Preprocessing and Quality Control
 
-A total of 60 missing denomination labels were restored from denomination-specific source files.
+1. Retain the seven original denomination-specific CSV files unchanged.
+2. Restore **380 missing `label` values** from known source-file provenance: 60 from the original six-denomination files and 320 from the new LKR 2000 file (16 complete scans). Do not infer these labels from sensor readings. The cleaned data includes an `original_label_missing` provenance flag.
+3. Remove rows with `sample_index == 0` from the derived stable view to exclude the observed sensor-settling transient. Apply this rule only where index 0 exists. **742 of 743** physical-note acquisitions contain index 0; one LKR 100 acquisition starts at index 2.
+4. Preserve the original sequence lengths. Seven physical-note acquisitions have 18 rather than 20 raw readings.
+5. For note-level classification, compute eight mean features from stable readings: the R/C, G/C, and B/C ratios for each sensor and the clear-channel mean for each sensor. The reference pipeline recomputes ratios from raw RGBC channels.
 
-For machine-learning evaluation, all measurements from the same physical banknote should remain in the same train, validation, or test partition.
+All clear-channel readings are positive. The source release and derived views should remain distinguishable so preprocessing is reproducible.
 
-Use denomination + note_id as the physical-banknote group identifier.
+## Baseline Technical Validation
 
-## Baseline Validation
+The seven-class reference baseline uses a `StandardScaler` followed by an RBF support vector machine (`C=5`, `gamma=0.06`) with eight note-level features.
 
-An RBF Support Vector Machine using eight scan-level features achieved:
+Evaluation uses a stratified **physical-banknote-level** split with 594 training notes and a separate 149-note holdout. Five repeats of stratified fivefold cross-validation run only on the training partition. No physical note appears in both partitions.
 
-- 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
+| Evaluation | Accuracy | Macro F1 |
+|---|---:|---:|
+| Training-only repeated fivefold CV (5 × 5) | 98.15% | 98.14% |
+| Separate 149-note holdout | 96.64% | 96.60% |
 
-These results are provided as technical validation of the dataset.
+Python-to-C++ prediction parity was **743/743** for note-level feature vectors and **742/742** for eligible raw-reading groups. This checks implementation agreement, not classification accuracy or performance on physical ESP32 hardware. After holdout evaluation, the companion deployment model was refitted on all 743 notes.
 
-## Intended Uses
+These results describe the recorded sensor setup and acquisition conditions; they do not establish performance under unmeasured banknote wear, lighting, orientation, or hardware variations.
 
-This dataset is intended for research in:
+## Intended Uses
 
-- banknote denomination recognition
-- embedded machine learning
-- color-sensor classification
-- assistive technology
-- feature engineering
-- group-aware machine-learning evaluation
+- Banknote denomination recognition.
+- Embedded machine learning and RGBC sensor classification.
+- Assistive currency-recognition research.
+- Feature engineering and acquisition-sequence analysis.
+- Leakage-free, physical-banknote-aware model evaluation.
 
 ## Out-of-Scope Uses
 
-The dataset is not intended for:
-
-- counterfeit detection
-- banknote authentication
-- financial security verification
-- banknote valuation
+- Counterfeit detection or banknote authentication.
+- Financial security verification.
+- Banknote valuation.
 
 ## License
 
-This dataset is released under the Creative Commons Attribution 4.0 International license.
+The dataset is licensed under the **Creative Commons Attribution 4.0 International (CC BY 4.0)** license. Software code, if distributed, should have its own explicitly stated license.
 
 ## Citation
 
-Citation information will be added after publication of the associated data paper.
+The permanent dataset DOI and associated data-paper citation will be added after publication.
 
 ## Dataset Version
 
-Version 1.0
+**Version 2.0 (release candidate):** Seven-denomination update including `2000.csv`. Version and citation details should be finalized with the published dataset record.