• All the data is available for download here
• The data is licensed under Creative Commons Attribution 4.0 (CC-BY-4.0), which means you are free to use, share, and adapt the data as long as you provide appropriate attribution.
• AWS sponsors the storage and data transfer costs of the data.
• Join discord server if you want to collaborate on this repo or have any questions.
• Be responsible, considerate, and think about the maintainers of the ecourts website. Avoid scraping with high concurrency.

Indian Court Judgments Downloader

This script automates the downloading of court judgments from the Indian eCourts website (https://judgments.ecourts.gov.in). It supports parallel downloading of judgments from multiple courts and date ranges.

Features

• Download judgments from specific courts or all available courts
• Specify date ranges for downloading or continue from last downloaded date
• Process date ranges in parallel for faster downloading
• Automatic CAPTCHA solving using OCR
• compresses PDFs using Ghostscript (on an average, we get about 50% compression)

Installation

1. Create and activate a virtual environment:

   python3.13 -m venv venv
   source venv/bin/activate

2. Install dependencies:

   pip install -r requirements.txt

3. Run the downloader:

   python download.py

Usage

Basic Usage

python download.py

This will download judgments from all courts, starting from the last downloaded date for each court (or from 2008-01-01 if no previous downloads).

Downloading from a Specific Court

python download.py --court_code "9~13"

This will download judgments from the specified court code, starting from the last downloaded date.

Downloading for a Specific Date Range

python download.py --start_date 2023-01-01 --end_date 2023-01-31

This will download judgments from all courts for the specified date range.

Downloading from a Specific Court for a Specific Date Range

python download.py --court_code "9~13" --start_date 2023-01-01 --end_date 2023-01-31

Controlling the Date Range Chunk Size

python download.py --court_code "9~13" --start_date 2023-01-01 --end_date 2023-12-31 --day_step 7

This will process the date range in 7-day chunks, which can be useful for parallel processing.

Court Codes

The following table lists the court codes used in the system:

Code	Court Name
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

How It Works

1. Task Generation: The script generates tasks based on court codes and date ranges. Each task represents a specific court and date range to process.

2. Parallel Processing: Tasks are processed in parallel using a thread pool, with a configurable number of worker threads.

3. Session Management: For each court and date range, the script:

   • Initializes a session with the eCourts website
   • Searches for judgments within the specified date range
   • Processes search results page by page
   • Downloads PDFs and associated metadata

4. CAPTCHA Handling: The script automatically solves CAPTCHAs using OCR:

   • Downloads CAPTCHA images
   • Uses EasyOCR to read the mathematical expressions
   • Solves the expressions and submits the answers
   • After 25 downloads, the script refreshes the session to avoid CAPTCHA challenges

5. Date Range Processing: The script processes judgments for the specified date range, with support for parallel processing across multiple courts.

File Structure

Downloaded judgments are stored in the ./data directory locally, organized by their original path structure from the eCourts website. For each judgment, the script saves:

• The PDF file
• A JSON metadata file containing details about the judgment

Example directory structure:

data/ ├── cnrorders/ │ ├── taphc/ │ │ ├── orders/ │ │ │ ├── 2017/ │ │ │ │ ├── HBHC010262202017_1_2047-06-29.pdf │ │ │ │ └── HBHC010262202017_1_2047-06-29.json

Processing Downloaded Metadata

After downloading judgments, you can process the metadata to extract structured information and generate a consolidated CSV file:

python process_metadata.py

This script:

1. Processes all JSON metadata files in the ./data directory
2. Extracts structured information from the raw HTML
3. Outputs information about any metadata files without raw HTML

Troubleshooting

• CAPTCHA Failures: If the script encounters issues with CAPTCHA solving, failed CAPTCHAs are saved in the ./captcha-failures directory for debugging.
• Session Expiry: The script automatically handles session expiry by refreshing tokens and cookies.
• Download Errors: Failed downloads are tracked and can be retried in subsequent runs.

the amount of storage required to download the data 1) by courts 2)by years is mentioned below

Summary of Data

1. Total Size by Court
2. Total Size by Year
3. Total Size

---

1. Total Size by Court

Code	Court	Total Size (GB)
3~22	High Court of Punjab and Haryana	137.17
33~10	Madras High Court	114.88
36~29	High Court for State of Telangana	92.80
28~2	High Court of Andhra Pradesh	87.84
10~8	Patna High Court	74.23
32~4	High Court of Kerala	72.25
8~9	High Court of Rajasthan	65.38
21~11	High Court of Orissa	65.03
9~13	Allahabad High Court	63.64
22~18	High Court of Chhattisgarh	56.08
27~1	Bombay High Court	53.13
29~3	High Court of Karnataka	47.76
23~23	High Court of Madhya Pradesh	37.87
7~26	High Court of Delhi	34.19
20~7	High Court of Jharkhand	29.28
19~16	Calcutta High Court	21.67
24~17	High Court of Gujarat	19.68
2~5	High Court of Himachal Pradesh	19.51
18~6	Gauhati High Court	16.01
1~12	High Court of Jammu and Kashmir	14.29
5~15	High Court of Uttarakhand	6.41
16~20	High Court of Tripura	3.61
14~25	High Court of Manipur	2.10
17~21	High Court of Meghalaya	1.28
11~24	High Court of Sikkim	0.62

---

2. Total Size by Year

Year	Total Size (GB)	Total Size (MB)
1950	0.00	2.87
1951	0.00	0.63
1952	0.00	0.69
1953	0.00	0.85
1954	0.00	2.06
1955	0.00	4.31
1956	0.00	2.23
1957	0.00	0.10
1958	0.00	2.57
1959	0.00	0.01
1960	0.00	0.20
1961	0.00	0.06
1962	0.00	0.04
1963	0.00	0.25
1964	0.00	0.06
1965	0.00	0.00
1966	0.00	0.05
1967	0.00	0.15
1968	0.00	0.10
1969	0.00	0.07
1970	0.00	0.39
1971	0.00	0.05
1972	0.00	0.05
1973	0.00	0.13
1974	0.00	0.08
1975	0.00	0.06
1976	0.00	0.33
1977	0.00	0.33
1978	0.01	9.33
1979	0.00	0.41
1980	0.00	0.92
1981	0.00	0.04
1982	0.00	1.19
1983	0.00	0.28
1984	0.00	0.20
1985	0.00	2.84
1986	0.00	0.54
1987	0.00	1.01
1988	0.00	0.81
1989	0.00	1.88
1990	0.00	1.94
1991	0.00	1.51
1992	0.00	2.62
1993	0.01	12.55
1994	0.00	3.28
1995	0.01	7.27
1996	0.03	27.10
1997	0.05	46.92
1998	0.20	200.13
1999	0.05	52.14
2000	0.09	91.01
2001	0.25	251.45
2002	0.22	224.48
2003	0.87	893.45
2004	1.56	1,595.21
2005	2.71	2,775.17
2006	4.62	4,731.73
2007	9.42	9,643.81
2008	11.27	11,544.32
2009	15.32	15,682.86
2010	20.85	21,353.52
2011	27.25	27,906.17
2012	27.56	28,225.60
2013	27.46	28,116.69
2014	31.05	31,794.33
2015	35.31	36,158.40
2016	37.03	37,923.79
2017	41.30	42,290.69
2018	62.14	63,628.12
2019	72.37	74,109.02
2020	53.07	54,348.66
2021	81.23	83,182.78
2022	124.62	127,610.14
2023	134.76	137,992.01
2024	124.78	127,773.17
2025	189.20	193,745.54

---

3. Total Across All Years

• Total Size: 1,163,985.75 MB
• Converted to GB: 1,136.70 GB
  (using 1 GB = 1,024 MB)
• Converted to TB: 1.110 TB
  (using 1 TB = 1,024*1,024 MB)

Key Insights

Insight	Value
Year with highest data volume	2025 (189.20 GB)
Court with highest data volume	3~22 (High Court of Punjab and Haryana) (137.17 GB)
Total data volume across all years	1,136.70 GB
Total data volume across all years (TB)	1.110 TB