1. No category

Training of
State and District Level
Master Trainers
On
SEEDING of Aadhaar in Electoral Rolls
15/05/15
Chief Electoral Officer, Tamil Nadu
NERPAP- Objectives
• Linking and authentication of EPIC data of
electors with Aadhaar No. of UIDAI,
• Encouraging the electors to Voluntarily disclose
multiple entries pertaining to the elector
• Correcting the errors or any relevant entry
pertaining to electors in the latest published
electoral roll on production of cogent
documentary evidence.
• Improving the image quality of an elector
• Obtaining the contact details of electors namelymobile / landline no. and e-mail id.
Information Collected under NERPAP
• Aadhaar information – Aadhaar number and
name as in Aadhaar
• Contact info – Mobile number , Alternate number
and Email ID
• Correction of the ER details – if correction is
required ,collect information in the requisite
format
• Voluntary disclosure of multiple entries
• Improving the health of ER by collecting details
relating to Absent, shifted and dead entry details.
Modes of Data Collection
• Electors voluntarily furnishing the
information through– NVS Portal
– E-mail
– SMS
– Call to 1950
 Door to door Collection by the BLO
Feeding of NERPAP Data
• Data entry operators fed data into ERMS portal
though the web service link
http://117.239.178.194/ERMS
• Feeding on cloud environment at a centralized
location facilitating simultaneous multiple nodes of
data entry.
• Prescreening of data is being done for the
correctness of information and system based
comparison
• entries requiring correction / additional information
are sent back to district to carryout necessary
modification.
Pre verification of NERPAP data
Aadhaar Numbers entered in NERPAP format
are checked for
• Typographic mistakes
• Junk entry
• Out-side TN Aadhaar no.- capturing details
and image.
Attributes for Comparison
1. Photograph as appearing in Electoral Roll and as
in NPR-Aadhaar data
2. Name as in Electoral Roll & NPR Database
3. Relation type & name i.e S/o, D/o, W/o and
Relation Name
4. Gender (M/F)
5. Date of Birth/Age
6. Address as in Electoral in & NPR Aadhaar
Database.
System based Comparison of fed Aadhaar
details with ER database
List
Details
List A
At least name match in the system based
ER and NPR Aadhaar data base comparison
List B
No system based match found in any
attribute of ER and NPR Aadhaar data base
List C
No Aadhaar no. Furnished by Elector
SAMPLE SCREEN
System based Comparison of fed Aadhaar
details with ER database
List
Match
Attributes
Seedablity
rating
List A1
Perfect Match
Name, Age, Gender, Relation
Type, Relation Name, Address
5 STAR
List A2
Simple Match
4 STAR
List A3
Partial Match
Name, Age, Gender, Relation
type, Relation
Name , Age, Gender
List A4
Probable Match
Name, Age
2 STAR
List A5
Doubtful Match
Only Name.
1 STAR
3 STAR
LIST A1
LIST A2
LIST A3
LIST A4
LIST A5
LIST B
SEEDING PROTOCOL
• Supervisor does the part wise pre-seeding
(100%) on web based software assisted by BLO
having Aadhaar card copies (if voluntarily given
by the elector).
• Each AERO verifies 4% of the total pre-seeded
Elector records randomly system generated
having minimum one record per part.
• ERO will verify 2% of the supervisor pre-seeded
records randomly generated by system for him,
covering all parts.
SEEDING PROTOCOL- contd…
Options- ‘Accept’, ‘Reject’, or ‘Schedule’ enquiry.
• Every ‘Accept' goes to next level of scrutinySupervisor->AERO->ERO
• Every 'Reject' entry goes back for verification to BLO
and if by AERO/ERO, the part for repeat pre-seeding
(100%) by the Supervisor & BLO.
• Every 'Schedule' comes up for enquiry with the
Elector on the special hearing days in presence of
the BLO for confirmation or rejection by AERO/ERO,
along with the claims and objections, if any.
Enquiry is either to confirm that the person
is same or to correct an entry in the ER.
Seeding decision making
Reject in cases 1. No other attributes matches including the photo
2. The Photo matches, but no other attributes matches
Schedule for Enquiry (name and photo matches) but
1. Photo similar, names different
2. Relation type same, but relation name is different
3. Gender alone is different, all other attributes are matching-If
gender in ER same as seems from photo seed/else Enquire.
4. Date of birth is not matching
5. Age group not matching
Requisite Claims forms to be collected from the elector and
should be disposed of immediately
Address not matching is not a reason for rejection.
In all other cases go for Accepting and Seeding
Seeding Examples
• Photo is visually matching and name given in Aadhaar
and ER fully or partially matching, gender being the
same, DOB /age and address being the same / address
being partially matching – it is a case for seeding
• Photo, Name, Age, Gender are all the same but
address doesn’t match – case for seeding
• DOB is not matching, but name, photo, relation type
and relation name match – case for seeding (verify for
DOB – is a case for Form 8)
• Both photo match, name is same, relation type and
relation name are different – case for seeding
Pre-seeding , Verification & Seeding Process
Pre-Seeding by Supervisors (100%)
Pre-seeding
module in
cloud
Re-verification,
and sending
back for Preseeding to be
done by
Supervisors
BLO
Verification
in cloud
Supervisor
-1 (8 to 10
Parts)
Supervisor
-2 (8 to 10
Parts)
Supervisor
-3 (8 to 10
Parts)
Verification by AEROs (8%)
Accepted
entries
Seeding by EROs (2%)
Seeding in
cloud
AERO -1
(4%
Random )
Accepted
entries
ERO (2%
Random )
Sched
ule
Enquir
y
Supervisor
-n (8 to 10
Parts)
Rejection by
Supervisors
Sched
ule
Enquir
y
AERO -2
(4%
Random ) Rejection by
AEROs
Goes back to BLO for Re-verification / Scheduled enquiry
Sched
ule
Enquir
y
Rejectio
n by
ERO
THANK
YOU