Indian High Court Judgments

An open dataset of judgments from 25 Indian High Courts, scraped from the eCourts judgments portal. The dataset is synced daily via GitHub Actions.

• Download the dataset: AWS Open Data Registry
• License: CC-BY-4.0 — free to use, share, and adapt with attribution
• Storage & transfer: sponsored by AWS
• Size: 17.8M judgments, ~1.25 TiB of S3 tar archives across all years. See STATS.md for a breakdown by court and year.
• Collaborate / ask questions: Discord

Please scrape responsibly. The eCourts portal is a public service — avoid high-concurrency scraping and be considerate of the maintainers. If you just want the data, use the AWS Open Data link above instead of running the scraper.

Please prefer tar archives over individual files. The bucket contains millions of PDFs and JSON files. Downloading each object individually is slow and creates a very large number of S3 requests. For bulk use, sync the data/tar/, metadata/tar/, or metadata/parquet/ prefixes and re-run the same aws s3 sync command periodically to fetch only changed objects.

Downloading from S3

The public bucket can be read without AWS credentials by passing --no-sign-request to the AWS CLI.

Prefer these paths:

• data/tar/ for judgment PDFs packaged as tar archives.
• metadata/tar/ for raw metadata JSON packaged as tar archives.
• metadata/parquet/ for structured metadata that can be queried with Athena, DuckDB, Spark, or pandas.

Examples:

# Download all PDF tar archives for one court
aws s3 sync s3://indian-high-court-judgments/data/tar/ ./data/tar/ \
  --exclude "*" \
  --include "*/court=27_1/*" \
  --no-sign-request

# Download all structured metadata parquet files
aws s3 sync s3://indian-high-court-judgments/metadata/parquet/ ./metadata/parquet/ \
  --no-sign-request

# Refresh a local mirror later; sync only transfers new or changed objects
aws s3 sync s3://indian-high-court-judgments/data/tar/ ./data/tar/ \
  --exclude "*" \
  --include "*/court=27_1/*" \
  --no-sign-request

Avoid syncing data/pdf/ or metadata/json/ for bulk use unless you specifically need individual objects. The tar and parquet layouts are the intended bulk-download interfaces.

Court codes

Court codes use the format STATE~COURT (e.g. 27~1 for Bombay). The canonical list lives in court-codes.json; this table is a human-readable mirror.

Code	Court
9~13	Allahabad High Court
27~1	Bombay High Court
19~16	Calcutta High Court
18~6	Gauhati High Court
36~29	High Court for State of Telangana
28~2	High Court of Andhra Pradesh
22~18	High Court of Chhattisgarh
7~26	High Court of Delhi
24~17	High Court of Gujarat
2~5	High Court of Himachal Pradesh
1~12	High Court of Jammu and Kashmir
20~7	High Court of Jharkhand
29~3	High Court of Karnataka
32~4	High Court of Kerala
23~23	High Court of Madhya Pradesh
14~25	High Court of Manipur
17~21	High Court of Meghalaya
21~11	High Court of Orissa
3~22	High Court of Punjab and Haryana
8~9	High Court of Rajasthan
11~24	High Court of Sikkim
16~20	High Court of Tripura
5~15	High Court of Uttarakhand
33~10	Madras High Court
10~8	Patna High Court

Running the scraper

The scraper is intended for contributors who maintain the dataset. End users should download from the AWS Open Data Registry link above.

Install

python3.13 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

PDF compression (optional) requires Ghostscript on PATH; on macOS: brew install ghostscript.

Usage

# Resume all courts from the last downloaded date per court
python download.py

# Specific court
python download.py --court_code "9~13"

# Specific date range across all courts
python download.py --start_date 2023-01-01 --end_date 2023-01-31

# Specific court + date range, chunked for parallelism
python download.py --court_code "9~13" --start_date 2023-01-01 --end_date 2023-12-31 --day_step 7

Features:

• Resumes from the last downloaded date per court when no range is given.
• Solves the eCourts arithmetic CAPTCHA automatically with an ONNX model.
• Compresses PDFs with Ghostscript (~50% average reduction) when gs is available.
• Parallelises across court × date chunks via a thread pool.

Processing metadata

After downloading, process_metadata.py parses the raw HTML embedded in each JSON metadata file and writes a consolidated Parquet file (snappy-compressed):

python process_metadata.py

Output: processed_metadata.parquet, plus per-court intermediate files under processed_data/.

Troubleshooting

• CAPTCHA failures: failed CAPTCHA images are saved to ./captcha-failures/ for debugging.
• Session expiry: handled automatically; the scraper refreshes its eCourts session every 25 downloads.
• Download errors: failed tasks are tracked and retried on subsequent runs.

For contributors

See AGENTS.md for architecture, data partitioning conventions, known gotchas, and CI notes.