---

name: chandra-ocr
description: >-
Extract text from complex documents using Chandra OCR — handles tables, forms, handwriting,
and full page layouts with high accuracy. Use when: extracting data from scanned documents,
reading complex tables from PDFs/images, processing handwritten forms.
license: Apache-2.0
compatibility: "Python 3.10+"
metadata:
author: terminal-skills
version: "1.0.0"
category: documents
tags:
- ocr
- document-processing
- tables
- handwriting
- data-extraction

Chandra OCR

Extract text from complex documents — tables, forms, handwriting, and full page layouts — using Chandra, a high-accuracy OCR engine built for real-world document complexity.

Overview

Chandra OCR handles the document types that trip up standard OCR: multi-column tables with merged cells, mixed print and handwriting, and complex page layouts. It outputs structured data (DataFrames, JSON) and supports GPU acceleration for batch processing.

Instructions

Installation

pip install chandra-ocr

For GPU acceleration (recommended for batch processing):

pip install chandra-ocr[gpu]

Basic Text Extraction

from chandra import OCR

ocr = OCR()

result = ocr.read("document.png")
print(result.text)

# From a PDF
result = ocr.read("report.pdf")
for page in result.pages:
    print(f"--- Page {page.number} ---")
    print(page.text)

Layout-Preserved Extraction

result = ocr.read("document.png", preserve_layout=True)

for block in result.blocks:
    print(f"Type: {block.type}")  # paragraph, table, header, handwriting
    print(f"Text: {block.text}")
    print(f"Confidence: {block.confidence:.2f}")

Table Extraction

result = ocr.read("invoice.png", extract_tables=True)

for table in result.tables:
    print(f"Table: {table.rows} rows x {table.cols} columns")
    df = table.to_dataframe()
    print(df.head())
    table.to_csv("extracted_table.csv")

Handwriting Recognition

result = ocr.read("handwritten_form.jpg", mode="handwriting")

for block in result.blocks:
    if block.type == "handwriting":
        print(f"Handwritten: {block.text} (conf: {block.confidence:.2f})")

Mixed Documents (Print + Handwriting)

result = ocr.read("filled_form.png", mode="mixed")

for block in result.blocks:
    print(f"[{block.type}] {block.text} (conf: {block.confidence:.2f})")

Batch Processing

import glob
from chandra import OCR
import json

ocr = OCR(device="cuda")
files = glob.glob("documents/*.pdf")

for file_path in files:
    result = ocr.read(file_path, extract_tables=True)
    output = {
        "file": file_path,
        "pages": len(result.pages),
        "text": result.text,
        "tables": [t.to_dict() for t in result.tables],
    }
    with open(file_path.replace(".pdf", ".json"), "w") as f:
        json.dump(output, f, indent=2)

Examples

Example 1: Extract Invoice Tables to CSV

from chandra import OCR

ocr = OCR()
result = ocr.read("invoice-2025-0342.pdf", extract_tables=True)

for i, table in enumerate(result.tables):
    df = table.to_dataframe()
    df.to_csv(f"invoice_table_{i}.csv", index=False)
    print(f"Table {i}: {table.rows} rows — columns: {list(df.columns)}")
# Output:
# Table 0: 12 rows — columns: ['Item', 'Qty', 'Unit Price', 'Total']
# Table 1: 3 rows — columns: ['Tax Type', 'Rate', 'Amount']

Example 2: Process Handwritten Medical Forms

from chandra import OCR
import requests

ocr = OCR()

result = ocr.read("patient_intake_form.jpg", mode="mixed", extract_tables=True)

extracted = {}
for block in result.blocks:
    extracted[block.label] = {
        "value": block.text,
        "confidence": block.confidence,
        "needs_review": block.confidence < 0.85,
    }

review_fields = {k: v for k, v in extracted.items() if v["needs_review"]}
print(f"Fields needing review: {list(review_fields.keys())}")
# Output:
# Fields needing review: ['allergies', 'signature']

Guidelines

• Use device="cuda" for batch processing — 5-10x faster than CPU
• Set dpi=300 or higher for scanned documents to improve accuracy
• For forms with checkboxes, use mode="mixed" to detect both print and marks
• Confidence threshold of 0.85 is a good default for human review routing
• Pre-process images (deskew, denoise) for better results on poor-quality scans

Option	Default	Description
mode	"auto"	Detection mode: auto, print, handwriting, mixed
preserve_layout	False	Maintain spatial positioning of text
extract_tables	False	Detect and extract tables as structured data
device	"cpu"	Processing device: cpu or cuda
language	"en"	Primary language hint
dpi	300	DPI for PDF rasterization