Datasets:

vennsa
/

SLR-NoteSense

label float64 20 5k ⌀	note_id int64 1 111	scan_id int64 1 111	sample_index int64 0 19	s1_r int64 45 2.84k	s1_g int64 37 1.96k	s1_b int64 36 979	s1_clear int64 72 5.44k	s1_nr float64 0.35 0.63	s1_ng float64 0.26 0.52	s1_nb float64 0.14 0.55	s2_r int64 31 3.62k	s2_g int64 28 4.12k	s2_b int64 27 2.25k	s2_clear int64 53 10.1k	s2_nr float64 0.24 0.59	s2_ng float64 0.31 0.61	s2_nb float64 0.17 0.52
100	1	1	0	1,668	931	513	3,064	0.544386	0.303851	0.167428	3,301	3,306	2,166	9,002	0.366696	0.367252	0.240613
100	1	1	1	1,662	928	511	3,054	0.544204	0.303864	0.167322	3,300	3,306	2,166	9,004	0.366504	0.36717	0.24056
100	1	1	2	1,658	926	510	3,047	0.544142	0.303905	0.167378	3,345	3,346	2,181	9,072	0.368717	0.368827	0.24041
100	1	1	3	1,659	928	512	3,047	0.54447	0.304562	0.168034	3,359	3,361	2,191	9,112	0.368635	0.368854	0.240452
100	1	1	4	1,667	931	513	3,063	0.544238	0.30395	0.167483	3,309	3,316	2,175	9,040	0.36604	0.366814	0.240597
100	1	1	5	1,672	934	514	3,072	0.544271	0.304036	0.167318	3,347	3,350	2,190	9,110	0.367398	0.367728	0.240395
100	1	1	6	1,687	942	519	3,099	0.544369	0.303969	0.167473	3,284	3,297	2,158	8,978	0.365783	0.367231	0.240365
100	1	1	7	1,695	948	521	3,115	0.544141	0.304334	0.167255	3,295	3,308	2,167	9,009	0.365745	0.367188	0.240537
100	1	1	8	1,697	950	522	3,120	0.54391	0.304487	0.167308	3,333	3,339	2,189	9,095	0.366465	0.367125	0.240682
100	1	1	9	1,694	947	521	3,112	0.544344	0.304306	0.167416	3,307	3,318	2,175	9,041	0.365778	0.366995	0.240571
100	1	1	10	1,707	955	525	3,137	0.54415	0.304431	0.167357	3,293	3,306	2,167	9,008	0.365564	0.367007	0.240564
100	1	1	11	1,700	951	523	3,125	0.544	0.30432	0.16736	3,287	3,300	2,162	8,985	0.365832	0.367279	0.240623
100	1	1	12	1,695	947	522	3,115	0.544141	0.304013	0.167576	3,289	3,303	2,164	8,992	0.36577	0.367326	0.240658
100	1	1	13	1,706	954	525	3,135	0.544179	0.304306	0.167464	3,344	3,352	2,195	9,127	0.366385	0.367262	0.240495
100	1	1	14	1,706	954	526	3,136	0.544005	0.304209	0.16773	3,365	3,370	2,211	9,175	0.366757	0.367302	0.240981
100	1	1	15	1,620	903	500	2,975	0.544538	0.303529	0.168067	3,346	3,354	2,205	9,143	0.365963	0.366838	0.241168
100	1	1	16	1,608	897	497	2,954	0.544347	0.303656	0.168246	3,342	3,352	2,201	9,133	0.365926	0.367021	0.240994
100	1	1	17	1,690	945	521	3,107	0.543933	0.304152	0.167686	3,314	3,326	2,185	9,064	0.365622	0.366946	0.241064
100	1	1	18	1,672	935	516	3,074	0.543917	0.304164	0.167859	3,328	3,333	2,193	9,093	0.365996	0.366546	0.241175
100	1	1	19	1,670	934	515	3,070	0.543974	0.304235	0.167752	3,443	3,440	2,246	9,318	0.3695	0.369178	0.241039
100	2	2	0	2,244	1,344	707	4,301	0.521739	0.312485	0.16438	3,587	3,587	2,045	9,419	0.380826	0.380826	0.217114
100	2	2	1	2,246	1,346	708	4,304	0.52184	0.312732	0.164498	3,562	3,562	2,041	9,405	0.378735	0.378735	0.217012
100	2	2	2	2,243	1,344	707	4,299	0.521749	0.312631	0.164457	3,523	3,525	2,034	9,392	0.375106	0.375319	0.216567
100	2	2	3	2,239	1,342	705	4,291	0.52179	0.312748	0.164297	3,518	3,519	2,031	9,377	0.375173	0.37528	0.216594
100	2	2	4	2,237	1,340	704	4,286	0.521932	0.312646	0.164256	3,515	3,518	2,029	9,369	0.375173	0.375494	0.216565
100	2	2	5	2,234	1,338	703	4,279	0.522085	0.31269	0.164291	3,511	3,514	2,027	9,362	0.375027	0.375347	0.216514
100	2	2	6	2,233	1,337	703	4,279	0.521851	0.312456	0.164291	3,517	3,519	2,031	9,375	0.375147	0.37536	0.21664
100	2	2	7	2,237	1,340	704	4,285	0.522054	0.312719	0.164294	3,521	3,522	2,032	9,380	0.375373	0.37548	0.216631
100	2	2	8	2,238	1,341	705	4,285	0.522287	0.312952	0.164527	3,522	3,523	2,033	9,384	0.37532	0.375426	0.216645
100	2	2	9	2,235	1,338	704	4,282	0.521952	0.312471	0.164409	3,516	3,518	2,031	9,376	0.375	0.375213	0.216617
100	2	2	10	2,236	1,339	704	4,281	0.522308	0.312777	0.164448	3,515	3,517	2,031	9,374	0.374973	0.375187	0.216663
100	2	2	11	2,235	1,339	704	4,281	0.522074	0.312777	0.164448	3,517	3,519	2,030	9,373	0.375227	0.37544	0.21658
100	2	2	12	2,234	1,338	703	4,278	0.522207	0.312763	0.164329	3,515	3,516	2,031	9,374	0.374973	0.37508	0.216663
100	2	2	13	2,232	1,337	703	4,277	0.521861	0.312602	0.164368	3,516	3,517	2,031	9,377	0.37496	0.375067	0.216594
100	2	2	14	2,231	1,336	703	4,275	0.521871	0.312515	0.164444	3,521	3,523	2,033	9,385	0.375173	0.375386	0.216622
100	2	2	15	2,238	1,341	705	4,288	0.521922	0.312733	0.164412	3,529	3,530	2,038	9,401	0.375386	0.375492	0.216785
100	2	2	16	2,240	1,342	706	4,294	0.521658	0.312529	0.164415	3,529	3,530	2,036	9,398	0.375505	0.375612	0.216642
100	2	2	17	2,239	1,342	705	4,292	0.521668	0.312675	0.164259	3,529	3,531	2,037	9,396	0.375585	0.375798	0.216794
100	2	2	18	2,239	1,341	705	4,291	0.52179	0.312515	0.164297	3,526	3,527	2,035	9,393	0.375386	0.375492	0.216651
100	2	2	19	2,238	1,341	705	4,288	0.521922	0.312733	0.164412	3,541	3,542	2,038	9,403	0.376582	0.376688	0.216739
100	3	3	0	1,755	814	446	2,984	0.588137	0.272788	0.149464	2,646	2,617	1,530	7,053	0.37516	0.371048	0.216929
100	3	3	1	1,757	815	447	2,988	0.588019	0.272758	0.149598	2,667	2,636	1,542	7,101	0.375581	0.371215	0.217153
100	3	3	2	1,758	815	447	2,990	0.58796	0.272575	0.149498	2,663	2,633	1,540	7,094	0.375388	0.371159	0.217085
100	3	3	3	1,758	815	447	2,990	0.58796	0.272575	0.149498	2,671	2,640	1,546	7,116	0.375351	0.370995	0.217257
100	3	3	4	1,762	817	448	2,996	0.588117	0.272697	0.149533	2,681	2,650	1,549	7,139	0.375543	0.3712	0.216977
100	3	3	5	1,762	818	448	2,998	0.587725	0.272849	0.149433	2,635	2,609	1,525	7,029	0.374876	0.371177	0.216958
100	3	3	6	1,763	818	449	2,998	0.588059	0.272849	0.149767	2,617	2,591	1,515	6,981	0.374875	0.37115	0.217018
100	3	3	7	1,765	819	449	3,003	0.587746	0.272727	0.149517	2,655	2,627	1,535	7,076	0.375212	0.371255	0.21693
100	3	3	8	1,767	820	449	3,005	0.58802	0.272879	0.149418	2,661	2,632	1,540	7,091	0.375264	0.371175	0.217177
100	3	3	9	1,766	819	449	3,003	0.588079	0.272727	0.149517	2,665	2,632	1,540	7,099	0.375405	0.370756	0.216932
100	3	3	10	1,765	819	449	3,004	0.58755	0.272636	0.149467	2,637	2,612	1,527	7,035	0.37484	0.371286	0.217058
100	3	3	11	1,766	819	449	3,002	0.588274	0.272818	0.149567	2,671	2,642	1,545	7,116	0.375351	0.371276	0.217116
100	3	3	12	1,765	819	449	3,002	0.587941	0.272818	0.149567	2,694	2,667	1,560	7,182	0.375104	0.371345	0.21721
100	3	3	13	1,766	819	449	3,002	0.588274	0.272818	0.149567	2,710	2,681	1,570	7,221	0.375294	0.371278	0.217421
100	3	3	14	1,768	820	450	3,007	0.587961	0.272697	0.149651	2,637	2,611	1,526	7,030	0.375107	0.371408	0.21707
100	3	3	15	1,768	820	449	3,006	0.588157	0.272788	0.149368	2,674	2,646	1,547	7,124	0.375351	0.371421	0.217153
100	3	3	16	1,770	821	450	3,009	0.588235	0.272848	0.149551	2,727	2,696	1,578	7,262	0.375516	0.371248	0.217296
100	3	3	17	1,773	822	450	3,016	0.587865	0.272546	0.149204	2,758	2,728	1,597	7,345	0.375494	0.371409	0.217427
100	3	3	18	1,777	824	452	3,023	0.587827	0.272577	0.14952	2,719	2,689	1,574	7,242	0.375449	0.371306	0.217343
100	3	3	19	1,776	824	452	3,022	0.58769	0.272667	0.14957	2,668	2,640	1,544	7,109	0.375299	0.37136	0.217189
100	4	4	0	1,766	946	494	3,210	0.550156	0.294704	0.153894	1,118	780	460	2,377	0.470341	0.328145	0.193521
100	4	4	1	1,768	947	495	3,216	0.549751	0.294465	0.153918	1,117	779	459	2,374	0.470514	0.328138	0.193345
100	4	4	2	1,760	942	492	3,199	0.550172	0.294467	0.153798	1,119	781	460	2,378	0.470564	0.328427	0.19344
100	4	4	3	1,763	944	493	3,205	0.550078	0.29454	0.153822	1,117	779	459	2,373	0.470712	0.328276	0.193426
100	4	4	4	1,758	941	492	3,196	0.550063	0.294431	0.153942	1,117	779	459	2,373	0.470712	0.328276	0.193426
100	4	4	5	1,755	939	491	3,190	0.550157	0.294357	0.153918	1,118	780	459	2,376	0.470539	0.328283	0.193182
100	4	4	6	1,759	942	492	3,199	0.549859	0.294467	0.153798	1,116	779	459	2,373	0.470291	0.328276	0.193426
100	4	4	7	1,759	942	492	3,198	0.550031	0.294559	0.153846	1,116	778	459	2,371	0.470687	0.328132	0.193589
100	4	4	8	1,760	942	492	3,198	0.550344	0.294559	0.153846	1,115	778	459	2,369	0.470663	0.328409	0.193753
100	4	4	9	1,761	943	492	3,201	0.550141	0.294595	0.153702	1,114	777	458	2,367	0.470638	0.328264	0.193494
100	4	4	10	1,762	943	493	3,203	0.550109	0.294411	0.153918	1,114	777	458	2,368	0.470439	0.328125	0.193412
100	4	4	11	1,760	941	492	3,199	0.550172	0.294154	0.153798	1,114	777	458	2,367	0.470638	0.328264	0.193494
100	4	4	12	1,750	936	489	3,182	0.549969	0.294155	0.153677	1,117	778	459	2,372	0.470911	0.327993	0.193508
100	4	4	13	1,734	927	484	3,152	0.550127	0.294099	0.153553	1,118	778	459	2,373	0.471134	0.327855	0.193426
100	4	4	14	1,729	924	482	3,142	0.550286	0.29408	0.153405	1,119	779	459	2,375	0.471158	0.328	0.193263
100	4	4	15	1,727	922	482	3,139	0.550175	0.293724	0.153552	1,120	780	459	2,378	0.470984	0.328007	0.193019
100	4	4	16	1,725	921	481	3,135	0.550239	0.29378	0.153429	1,121	781	460	2,380	0.471008	0.328151	0.193277
100	4	4	17	1,726	922	481	3,135	0.550558	0.294099	0.153429	1,119	779	459	2,376	0.47096	0.327862	0.193182
100	4	4	18	1,726	922	482	3,136	0.550383	0.294005	0.153699	1,121	781	460	2,381	0.470811	0.328013	0.193196
100	4	4	19	1,726	922	482	3,137	0.550207	0.293911	0.15365	1,120	780	460	2,378	0.470984	0.328007	0.19344
100	5	5	0	2,004	1,040	553	3,569	0.561502	0.291398	0.154945	2,853	2,446	1,350	6,752	0.422541	0.362263	0.199941
100	5	5	1	2,002	1,038	552	3,564	0.561728	0.291246	0.154882	2,837	2,420	1,339	6,695	0.423749	0.361464	0.2
100	5	5	2	2,002	1,037	552	3,563	0.561886	0.291047	0.154926	2,835	2,416	1,338	6,688	0.423894	0.361244	0.20006
100	5	5	3	2,004	1,039	553	3,567	0.561817	0.291281	0.155032	2,839	2,419	1,339	6,696	0.423984	0.36126	0.19997
100	5	5	4	2,006	1,041	553	3,573	0.561433	0.291352	0.154772	2,842	2,422	1,341	6,705	0.423863	0.361223	0.2
100	5	5	5	2,000	1,037	552	3,561	0.56164	0.29121	0.155013	2,826	2,402	1,331	6,654	0.424707	0.360986	0.20003
100	5	5	6	1,995	1,034	550	3,551	0.561814	0.291186	0.154886	2,822	2,395	1,328	6,640	0.425	0.360693	0.2
100	5	5	7	1,991	1,031	548	3,543	0.561953	0.290996	0.154671	2,773	2,339	1,300	6,501	0.42655	0.359791	0.199969
100	5	5	8	1,981	1,025	546	3,524	0.562145	0.290863	0.154938	2,720	2,279	1,270	6,352	0.428212	0.358785	0.199937
100	5	5	9	1,972	1,020	543	3,506	0.562464	0.29093	0.154877	2,715	2,275	1,268	6,341	0.428166	0.358776	0.199968
100	5	5	10	1,985	1,029	547	3,533	0.561845	0.291254	0.154826	2,747	2,308	1,285	6,424	0.427615	0.359278	0.200031
100	5	5	11	1,998	1,036	551	3,558	0.561551	0.291175	0.154862	2,743	2,292	1,279	6,409	0.427992	0.357622	0.199563
100	5	5	12	1,995	1,034	550	3,551	0.561814	0.291186	0.154886	2,755	2,315	1,289	6,445	0.427463	0.359193	0.2
100	5	5	13	2,005	1,040	553	3,571	0.561467	0.291235	0.154859	2,811	2,380	1,322	6,608	0.425393	0.36017	0.200061
100	5	5	14	1,997	1,034	551	3,555	0.561744	0.290858	0.154993	2,795	2,363	1,313	6,563	0.425872	0.360049	0.200061
100	5	5	15	1,987	1,029	548	3,537	0.561776	0.290925	0.154934	2,801	2,377	1,319	6,593	0.424845	0.360534	0.200061
100	5	5	16	2,006	1,041	554	3,574	0.561276	0.29127	0.155008	2,807	2,377	1,320	6,597	0.425496	0.360315	0.200091
100	5	5	17	1,984	1,026	546	3,529	0.562199	0.290734	0.154718	2,789	2,364	1,312	6,559	0.425217	0.360421	0.20003
100	5	5	18	1,984	1,027	547	3,531	0.56188	0.290852	0.154914	2,757	2,327	1,293	6,465	0.42645	0.359938	0.2
100	5	5	19	1,983	1,026	546	3,529	0.561916	0.290734	0.154718	2,771	2,343	1,301	6,504	0.426046	0.36024	0.200031

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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

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