PaddleOCR vs Tesseract: compare the pipeline you need

Tesseract: a small, explicit baseline

Install the native Tesseract engine and the required language files before installing the Python wrapper. Page segmentation mode changes the assumption about layout: a single uniform block and a sparse page should not automatically use the same setting.

Check resolution, skew, borders, binarization and the source image before interpreting a failure as a model limitation. Save both text and coordinates if your downstream task needs source evidence.

Documentation-based example; no runtime measurement is claimed.

# pip install pytesseract pillow
# Install native Tesseract and English traineddata separately.
import pytesseract
from PIL import Image

image = Image.open("scan.png")
print(pytesseract.image_to_string(image, lang="eng", config="--psm 6"))
# --psm 6 assumes one uniform text block; choose a suitable mode for your page.

PaddleOCR: use the current documented interface

The checked documentation uses predict(), followed by result methods such as save_to_json(). Select the recognition language and record the model identity. Install PaddlePaddle using the platform-specific runtime instructions.

PaddleOCR-VL and document parsing are separate configurations from the general OCR pipeline below. Do not transfer a document-parsing benchmark score to this text-recognition example.

Documentation-based example; no runtime measurement is claimed.

from paddleocr import PaddleOCR

ocr = PaddleOCR(
    lang="en",
    use_doc_orientation_classify=False,
    use_doc_unwarping=False,
    use_textline_orientation=False,
)
for result in ocr.predict("scan.png"):
    result.save_to_json("output")

Add document parsers only when the output requires them

If you need reading order, tables, formulas or Markdown, include a document-parsing candidate such as Docling or MinerU. Keep plain transcription, layout reconstruction and structured extraction as separate scores.

EasyOCR and RapidOCR can also serve as image-text baselines. A general-purpose VLM is another candidate to measure when the layout demands it, with explicit checks for missing text and invented content. None is a universal default established by the old single-invoice examples.

A fair comparison needs both quality and deployment data

Use the same held-out images, language, resolution and normalization. Report CER/WER with the exact split and model configurations. For invoices, add schema field and line-item scores; for tables, add structure scores.

Time cold initialization, warm inference and end-to-end processing separately. Record CPU/GPU, threads, model checkpoint, batch size, memory, failures and retries. A library name alone is not enough to reproduce a speed claim.