IMLJD — Indian Matrimonial Litigation Judgment Dataset

Computational dataset of 4897 Indian court judgments on matrimonial disputes (IPC 498A, DV Act, CrPC 482 quashing petitions), built from AWS Open Data judicial archives.

Dataset Description

Sub-corpus	Cases	Court	Period	Precision
SC matrimonial	1,474	Supreme Court of India	2000–2024	Medium (broad filter)
Karnataka HC	2,139	Karnataka High Court	2018–2024	High (482 confirmed)
Total	3,613
Combined HC	3,423	Karnataka + Delhi + others	2018–2024	Mixed
Grand Total	4,897

Key Statistics

Metric	Value
Total cases	3,613
SC quash success rate	57.6%
HC (Karnataka) quash success rate	39.7%
Cases with CrPC 482	2,179
Cases with IPC 498A	192
KG nodes	1,520
KG edges	13,364

Columns

SC sub-corpus (sc_enriched.csv)

Column	Description
case_id	Stable identifier
title	Case title
petitioner / respondent	Party names
year	Year (2000–2024)
case_type	quash / appeal / maintenance / bail / other
outcome	quashed / allowed / dismissed / settled / disposed / partly_allowed
statutes	Pipe-delimited statute list
disposal_nature	Raw disposal string
mediation_mentioned	bool
settlement_mentioned	bool
relatives_accused	bool
judicial_criticism_misuse	bool
arnesh_kumar_cited	bool
rajesh_sharma_cited	bool

HC Karnataka sub-corpus (hc_karnataka.csv)

Column	Description
title	Case title (CRL.P/NNNNN/YYYY format)
judge	Presiding judge
decision_date	Date of judgment
disposal_nature	ALLOWED / DISMISSED / DISPOSED / Partly Allowed
outcome	quashed / dismissed / disposed / partly_allowed
_year	Year (2018–2024)
_bench	Bench (karhcdharwad / karhckalaburagi / karnataka_bng_old)
statutes	CrPC 482 (all cases)
case_type	quash (all cases)

Data Sources

Both sub-corpora built from AWS Open Data (no credentials needed):

• s3://indian-supreme-court-judgments/
• s3://indian-high-court-judgments/

Usage

from datasets import load_dataset

# Supreme Court cases
sc = load_dataset("joyboseroy/imljd", "sc")

# Karnataka HC quash petitions  
hc = load_dataset("joyboseroy/imljd", "hc_karnataka")

# Basic analysis
import pandas as pd
df = pd.DataFrame(sc["train"])
quash = df[df["case_type"] == "quash"]
print(f"SC quash success rate: {(quash['outcome']=='quashed').mean()*100:.1f}%")

Knowledge Graph

A NetworkX/GEXF knowledge graph is included in the repository:

• Nodes: Case, Statute, Court, Outcome, Precedent, Year
• Edges: INVOKES, HEARD_BY, RESULTS_IN, CITES, DECIDED_IN

Open data/kg/imljd_graph.gexf in Gephi for visualisation.

Ethical Considerations

• Public court judgments only
• Names present as in original public records
• Recommended: anonymisation pass before NLP model training
• Not suitable for "false case detection" — ground truth doesn't exist cleanly
• Framing: procedural fairness research, not case outcome prediction

Citation

@dataset{boseroy2026imljd,
  title   = {IMLJD: Indian Matrimonial Litigation Judgment Dataset},
  author  = {Bose, Joy},
  year    = {2026},
  url     = {https://huggingface.co/datasets/joyboseroy/imljd},
  note    = {3,613 cases, Supreme Court 2000-2024 and Karnataka HC 2018-2024}
}

Related work:

@article{boseroy2026falkor,
  title  = {FalkorDB-IRAC: Graph-Grounded Legal Reasoning},
  author = {Bose, Joy},
  year   = {2026},
  url    = {https://arxiv.org/abs/2605.14665}
}