PaddleOCR vs EasyOCR: Initialization Time Killed My Production Pipeline

The Problem Nobody Mentions in Benchmarks

Most OCR comparisons focus on accuracy and inference speed. But here’s what broke my production pipeline: PaddleOCR takes 8-12 seconds to initialize on CPU, while EasyOCR needs 15-20 seconds. When you’re running batch jobs or serverless functions, that startup cost isn’t a footnote—it’s the entire story.

I hit this building a document processing API. The plan was simple: spin up containers on demand, OCR incoming invoices, return structured data. EasyOCR worked beautifully in local tests. Then I deployed it and watched my cold start times blow past 30 seconds. Users don’t wait 30 seconds. They refresh the page, retry the upload, and eventually leave.

This isn’t about which library has better CER (Character Error Rate) on ICDAR2015. It’s about whether your service can actually respond before the client times out.

Measuring What Actually Matters

I benchmarked both libraries on three machines: a 2021 M1 MacBook Pro, an AWS t3.medium (2 vCPU, 4GB RAM), and a Raspberry Pi 4 (4GB). The initialization time includes model download (first run only) and loading weights into memory.

Here’s the timing code I used:

import time
import sys

def measure_init(library_name, init_func):
    """Time the initialization of an OCR library."""
    start = time.perf_counter()
    try:
        reader = init_func()
        elapsed = time.perf_counter() - start
        print(f"{library_name} initialized in {elapsed:.2f}s")
        return reader, elapsed
    except Exception as e:
        print(f"{library_name} failed: {e}")
        sys.exit(1)

# PaddleOCR
from paddleocr import PaddleOCR
paddle_reader, paddle_time = measure_init(
    "PaddleOCR",
    lambda: PaddleOCR(use_angle_cls=True, lang='en', show_log=False)
)

# EasyOCR  
import easyocr
easy_reader, easy_time = measure_init(
    "EasyOCR",
    lambda: easyocr.Reader(['en'], gpu=False, verbose=False)
)

print(f"\nDelta: {abs(easy_time - paddle_time):.2f}s")
print(f"Winner: {'PaddleOCR' if paddle_time < easy_time else 'EasyOCR'}")

On my M1 MacBook (macOS 13.2, Python 3.11), PaddleOCR consistently initialized in 7.8-8.3 seconds. EasyOCR took 14.2-15.8 seconds. That’s roughly 2x slower.

But the gap widens on weaker hardware. On the AWS t3.medium (Ubuntu 22.04), PaddleOCR took 11.4 seconds while EasyOCR needed 23.7 seconds. The Raspberry Pi 4 was painful: PaddleOCR clocked in at 28 seconds, EasyOCR at 51 seconds. If you’re deploying to edge devices or budget cloud instances, initialization time compounds fast.

Why the Difference?

EasyOCR loads multiple models at initialization: a text detection model (CRAFT by default) and a recognition model (based on the languages you specify). Both are PyTorch models serialized with torch.save(). Loading them involves deserializing the model architecture, loading weights, and moving tensors to the target device. On CPU, PyTorch’s eager execution overhead adds up—especially when the models are large.

PaddleOCR uses PaddlePaddle (Baidu’s framework), which has leaner CPU inference paths. The model files are smaller (the English det+rec+cls combo is ~20MB vs EasyOCR’s ~40MB), and PaddlePaddle’s inference library is optimized for embedded devices. Initialization still involves loading three separate models, but the framework overhead is lower.

There’s also a difference in what gets loaded. EasyOCR’s CRAFT detector is powerful but heavy. PaddleOCR’s DB (Differentiable Binarization) detector is lighter and faster to initialize. If you’re using multiple languages in EasyOCR, you’re loading a separate recognition model per language—initialization time scales linearly.

Inference Speed: A Different Story

Once initialized, inference speed is competitive. I tested both on a batch of 50 invoice images (mixed English text, tables, handwriting). Image sizes ranged from 800×600 to 2400×3200 pixels.

import glob
from PIL import Image

images = [Image.open(p) for p in glob.glob("invoices/*.jpg")]

# PaddleOCR inference
start = time.perf_counter()
for img in images:
    result = paddle_reader.ocr(img, cls=True)
    # result is a list of [bbox, (text, confidence)]
paddle_inference = time.perf_counter() - start
print(f"PaddleOCR: {paddle_inference:.2f}s total, {paddle_inference/len(images):.3f}s per image")

# EasyOCR inference
start = time.perf_counter()
for img in images:
    result = easy_reader.readtext(img)
    # result is a list of (bbox, text, confidence)
easy_inference = time.perf_counter() - start
print(f"EasyOCR: {easy_inference:.2f}s total, {easy_inference/len(images):.3f}s per image")

On the M1 MacBook, PaddleOCR processed the batch in 47.3 seconds (0.946s per image). EasyOCR took 52.1 seconds (1.042s per image). The gap is there, but it’s not dramatic—about 10% faster for PaddleOCR.

On the t3.medium, the results flipped slightly. PaddleOCR took 89.2 seconds, EasyOCR 84.6 seconds. I’m not entirely sure why EasyOCR was faster here—possibly because CRAFT’s detection quality reduced false positives, so the recognition stage had less work. Or maybe PyTorch’s CPU optimizations kicked in better on x86. Either way, the difference was under 6%.

What’s clear: if you’re processing hundreds of images in a single session, initialization time becomes noise. But if you’re doing one-off requests (APIs, serverless functions, Jupyter notebooks), that 8-15 second startup is the entire user experience.

Memory Footprint

Another surprise: EasyOCR’s memory usage spiked higher during initialization. On the t3.medium instance, I monitored RSS (Resident Set Size) with psutil:

import psutil
import os

process = psutil.Process(os.getpid())
print(f"Memory before init: {process.memory_info().rss / 1024**2:.1f} MB")

reader = easyocr.Reader(['en'], gpu=False, verbose=False)
print(f"Memory after EasyOCR init: {process.memory_info().rss / 1024**2:.1f} MB")

EasyOCR pushed memory usage to 1.8 GB after initialization. PaddleOCR stayed around 950 MB. If you’re running on a 1GB RAM container (common in serverless tiers), EasyOCR will OOM before you even run inference.

This matters for cost optimization. AWS Lambda charges by memory allocation. A function that needs 2GB costs twice as much as one that needs 1GB. Over millions of invocations, that adds up.

Accuracy: Mostly a Wash

I tested both on the SROIE dataset (receipts) and a custom set of 100 invoices I manually labeled. For clean, high-resolution text, both libraries achieved >95% word-level accuracy. PaddleOCR was slightly better on rotated text (thanks to the angle classification model), while EasyOCR handled low-contrast images better (CRAFT’s feature extraction is robust to lighting variations).

But honestly? The accuracy gap is small enough that it won’t matter for most use cases. If you need sub-5% CER on a specific domain, you’ll probably fine-tune a model anyway—neither library’s pretrained weights will cut it out of the box.

What I found more interesting: PaddleOCR’s output format is messier. It returns bounding boxes as nested lists of coordinates, and the text/confidence is a tuple inside another list. EasyOCR’s output is cleaner: a flat list of (bbox, text, confidence) tuples. If you’re chaining OCR into downstream NLP pipelines, that API difference matters.

When to Use Which

Use PaddleOCR if:
– You’re deploying to serverless/edge environments where cold start time dominates
– Memory is constrained (<2GB)
– You need faster initialization for batch jobs or interactive tools
– You’re okay with a slightly clunkier API

Use EasyOCR if:
– You’re running long-lived services where initialization happens once
– You need multi-language support (EasyOCR supports 80+ languages, PaddleOCR supports ~20)
– You prefer PyTorch (easier to fine-tune, better ecosystem integration)
– You’re already using PyTorch models downstream and want to avoid framework mixing

For my production API, I ended up using PaddleOCR. The 8-second cold start was acceptable; 20 seconds wasn’t. I also implemented lazy loading—only initialize the OCR engine when the first request arrives, not at container startup. That shaved another 3-4 seconds off the perceived latency for the first user.

But I’m still not thrilled with 8 seconds. Next up: quantization, model pruning, and whether ONNX Runtime can cut that in half. If you’ve tried compiling PaddleOCR models to ONNX, I’d be curious how much speedup you saw—I haven’t found great docs on this yet.

The Real Bottleneck

Here’s the thing: neither library is slow because of bad code. They’re slow because loading 20-40MB of model weights from disk, deserializing them, and allocating tensors in memory is fundamentally expensive. If you’ve worked with quantization techniques for mobile models, you’ll recognize this problem—on-device inference always trades off model size, accuracy, and latency.

The initialization cost is unavoidable unless you either (1) keep the process alive between requests, or (2) preload models into a shared memory space. Option 1 is what long-running servers do. Option 2 is what GPU inference servers like vLLM do for LLMs—you can read more in my vLLM vs TensorRT-LLM comparison, though that’s a different scale of problem.

For OCR, the best optimization is architectural: batch your requests, reuse processes, and avoid cold starts. But if you’re stuck with a cold-start environment, PaddleOCR’s 2x initialization advantage is the difference between usable and unusable.

In Part 2, I’ll dig into what actually works to cut down that 8-second init time: INT8 quantization, ONNX export, model distillation, and a few hacks involving preloading models into tmpfs. Some of them worked better than I expected. One of them made things worse.