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

bash
pip install chandra-ocr

For GPU acceleration (recommended for batch processing):

bash
pip install chandra-ocr[gpu]

Basic Text Extraction

python
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

python
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

python
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

python
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)

python
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

python
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

python
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

python
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 |