Benchmarking OCR APIs on Real-World Documents

With the speedy developments in Giant Language Fashions (LLMs) and Imaginative and prescient-Language Fashions (VLMs), many consider OCR has develop into out of date. If LLMs can “see” and “learn” paperwork, why not use them immediately for textual content extraction?

The reply lies in reliability. Are you able to all the time be a 100% certain of the veracity of textual content output that LLMs interpret from a doc/picture? We put this to check with a easy experiment. We requested colleagues to make use of any LLM of their option to extract a listing of passenger names (10) from a pattern PDF flight ticket.

Whereas LLMs can interpret and summarize paperwork, they lack the precision and structured output required for important enterprise purposes the place 100% information accuracy is essential. Moreover, LLMs require vital computational assets, making them expensive and impractical for large-scale doc processing, particularly in enterprise and edge deployments.

OCR, then again, is optimized for effectivity, operating on low-power gadgets whereas delivering constant outcomes. When accuracy is non-negotiable whether or not in monetary information, authorized contracts, or regulatory compliance, OCR stays probably the most reliable answer.

In contrast to LLMs, OCR APIs present confidence scores and bounding packing containers, permitting builders to detect uncertainties in extracted textual content. This degree of management is essential for companies that can’t afford incorrect or hallucinated information. That’s why OCR APIs proceed to be broadly utilized in doc automation workflows, AI-driven information extraction, and enterprise purposes.

To evaluate the state of OCR in 2025, we benchmarked 9 of the most well-liked OCR APIs, masking business options, open-source OCR engines, and doc processing frameworks. Our objective is to supply an goal, data-driven comparability that helps builders and enterprises select the perfect instrument for his or her wants.

Methodology

Dataset Choice:

To make sure a complete analysis of OCR APIs or OCR fashions in real-world eventualities, we chosen datasets that embody a various vary of doc varieties and challenges generally encountered in sensible purposes. Our dataset selections embody:

• Frequent Enterprise Paperwork: Types, invoices, and monetary statements containing structured textual content.
• Receipts: Printed transaction slips with various fonts, noise, and pale textual content.
• Low-Decision Photos: Paperwork captured underneath suboptimal situations, mimicking real-world scanning and pictures limitations.
• Handwritten Textual content: Samples with totally different handwriting types to check handwriting recognition capabilities.
• Blurred or Distorted Textual content: Photos with movement blur or compression artifacts to evaluate OCR robustness.
• Rotated or Skewed Textual content: Paperwork scanned or photographed at an angle, requiring superior textual content alignment dealing with.
• Tabular Information: Paperwork containing structured tabular info, difficult for OCR fashions to protect structure integrity.
• Dense Textual content: Textual content-heavy paperwork, corresponding to account opening types, to judge efficiency in high-content areas.

To make sure our benchmark covers all these real-world challenges, we choose the next datasets:

1. STROIE (link to dataset)

2. FUNSD (link to dataset)

These datasets present a complete testbed for evaluating OCR efficiency throughout sensible and actual life eventualities.

Fashions Choice

To guage OCR efficiency throughout totally different eventualities, we embody a mixture of business APIs, open-source OCR fashions, and doc processing frameworks. This ensures a balanced comparability between proprietary options and freely obtainable options. The fashions utilized in our benchmark are:

• Well-liked Business OCR APIs:
  • Google Cloud Imaginative and prescient AI
  • Azure AI Doc Intelligence
  • Amazon Textract
• Well-liked Open-Supply OCR APIs:
  • Surya
  • PaddleOCR
  • RapidOCR
  • Extractous
• Well-liked Open-Supply Doc Processing Frameworks:

To display how every OCR API processes a picture, we offer code snippets for operating OCR utilizing each business APIs and open-source frameworks. These examples present tips on how to load a picture, apply OCR, and extract the textual content, providing a sensible information for implementation and comparability. Under are the code snippets for every mannequin:

1. Google Cloud Imaginative and prescient AI: First step is to arrange a brand new Google Cloud Undertaking. Within the Google Cloud Console, navigate to APIs & Companies → Library, seek for Imaginative and prescient API, and click on Allow. Go to APIs & Companies → Credentials, click on Create Credentials → Service Account, title it (e.g., vision-ocr-service), and click on Create & Proceed. Assign the Proprietor (or Editor) position and click on Carried out. Now, in Service Accounts, choose the account, go to Keys → Add Key → Create New Key, select JSON, and obtain the .json file.

Required Packages:

pip set up google-cloud-vision

from google.cloud import imaginative and prescient
from google.oauth2 import service_account

credentials = service_account.Credentials.from_service_account_file("/content material/ocr-nanonets-cea4ddeb1dd2.json") #path to the json file downloaded

consumer = imaginative and prescient.ImageAnnotatorClient(credentials=credentials)

def detect_text(image_path):
   """Detects textual content in a picture utilizing Google Cloud Imaginative and prescient AI."""
   with open(image_path, 'rb') as image_file:
       content material = image_file.learn()

   picture = imaginative and prescient.Picture(content material=content material)
   response = consumer.text_detection(picture=picture)
  
   texts = response.text_annotations

   if texts:
       return texts[0].description
   else:
       return "No textual content detected."

   if response.error.message:
       elevate Exception(f"Error: {response.error.message}")

# Exchange along with your picture path
image_path = "/content material/drive/MyDrive/OCR_datasets/STROIE/test_data/img/X00016469670.jpg"
print(detect_text(image_path))

2. Azure AI Doc Intelligence: Create an Azure Account (Azure Portal) to get $200 free credit for 30 days. Within the Azure Portal, go to Create a Useful resource, seek for Azure AI Doc Intelligence (Type Recognizer), and click on Create. Select a Subscription, Useful resource Group, Area (nearest to you), set Pricing Tier to Free (if obtainable) or Customary, then click on Evaluation + Create → Create. As soon as created, go to the Azure AI Doc Intelligence useful resource, navigate to Keys and Endpoint, and duplicate the API Key and Endpoint.

Required Packages:

pip set up azure-ai-documentintelligence

from azure.ai.documentintelligence import DocumentIntelligenceClient
from azure.core.credentials import AzureKeyCredential
import io
# Exchange along with your Azure endpoint and API key
AZURE_ENDPOINT = "https://your-region.api.cognitive.microsoft.com/"
AZURE_KEY = "your-api-key"

consumer = DocumentIntelligenceClient(AZURE_ENDPOINT, AzureKeyCredential(AZURE_KEY))
def extract_text(image_path):
   """Extracts textual content from a picture utilizing Azure AI Doc Intelligence."""
   with open(image_path, "rb") as image_file:
       image_data = image_file.learn()
       
   poller = consumer.begin_analyze_document("prebuilt-read", doc=image_data)
   
   consequence = poller.consequence()
   extracted_text = []
   for web page in consequence.pages:
       for line in web page.strains:
           extracted_text.append(line.content material)

   print("Detected textual content:")
   print("n".be part of(extracted_text))
   
image_path = image_path
extract_text(image_path)

3. Amazon Textract: Create an AWS Account (AWS Sign-Up) to entry Amazon Textract’s free-tier (1,000 pages/month for 3 months). Within the AWS Administration Console, go to IAM (Identification & Entry Administration) → Customers → Create Person, title it (e.g., textract-user), and choose Programmatic Entry. Below Permissions, connect AmazonTextractFullAccess and AmazonS3ReadOnlyAccess (if utilizing S3). Click on Create Person and duplicate the Entry Key ID and Secret Entry Key.

Required Packages:

pip set up boto3

Set Setting Variables:

export AWS_ACCESS_KEY_ID="your-access-key"
export AWS_SECRET_ACCESS_KEY="your-secret-key"
export AWS_REGION="your-region"

import boto3
textract = boto3.consumer("textract", region_name="us-east-1")

def extract_text(image_path):
   """Extracts textual content from a picture utilizing Amazon Textract."""
   with open(image_path, "rb") as image_file:
       image_bytes = image_file.learn()

   response = textract.detect_document_text(Doc={"Bytes": image_bytes})
   extracted_text = []
   for merchandise in response["Blocks"]:
       if merchandise["BlockType"] == "LINE":
           extracted_text.append(merchandise["Text"])
   print("Detected textual content:")
   print("n".be part of(extracted_text))
   
image_path = image_path
extract_text(image_path)

4. Surya :  Use pip set up surya-ocr to obtain the mandatory packages. Then create a python file with the next code and run it in terminal.

from PIL import Picture
from surya.recognition import RecognitionPredictor
from surya.detection import DetectionPredictor

picture = Picture.open(image_path)
langs = ["en"]

recognition_predictor = RecognitionPredictor()
detection_predictor = DetectionPredictor()

predictions = recognition_predictor([image], [langs], detection_predictor)

5. PaddleOCR : Use “pip set up paddleocr paddlepaddle” to put in the required packages. Then create a python file with the next code and run it in terminal.

from paddleocr import PaddleOCR

ocr = PaddleOCR(use_angle_cls=True, lang="en")

consequence = ocr.ocr(image_path, cls=True)

6. RapidOCR : Use “pip set up rapidocr_onnxruntime” to put in the required packages. Then create a python file with the next code and run it in terminal.

from rapidocr_onnxruntime import RapidOCR

engine = RapidOCR()

img_path = image_path
consequence, elapse = engine(img_path)

7. Extractous: Use “sudo apt set up tesseract-ocr tesseract-ocr-deu” to put in the required packages. Then create a python file with the next code and run it in terminal.

from extractous import Extractor, TesseractOcrConfig
extractor = Extractor().set_ocr_config(TesseractOcrConfig().set_language("en"))

consequence, metadata = extractor.extract_file_to_string(image_path)

print(consequence)

8. Marker: Use “pip set up marker-pdf” to put in the required packages. Then in terminal use the next code.

!marker_single image_path --output_dir saving_directory --output_format json

9. Unstructured-IO: Use “pip set up “unstructured[image]”” to put in the required packages. Then create a python file with the next code and run it in terminal.

from unstructured.partition.auto import partition

components = partition(filename=image_path)

print("nn".be part of([str(el) for el in elements]))

Analysis Metrics

To evaluate the effectiveness of every OCR mannequin, we consider each accuracy and efficiency utilizing the next metrics:

• Character Error Charge (CER): Measures the ratio of incorrect characters (insertions, deletions, and substitutions) to the full characters within the floor fact textual content. Decrease CER signifies higher accuracy.
• Phrase Error Charge (WER): Just like CER however operates on the phrase degree, calculating errors relative to the full variety of phrases. It helps assess how nicely fashions acknowledge full phrases.
• ROUGE Rating: A textual content similarity metric that compares OCR output with the bottom fact based mostly on overlapping n-grams, capturing each precision and recall.

For efficiency analysis, we measure:

• Inference Time (Latency per Picture): The time taken by every mannequin to course of a single picture, indicating pace and effectivity in real-world purposes.

Value Analysis:

• For business OCR APIs, value is decided by their pricing fashions, sometimes based mostly on the variety of processed pages or pictures. 
• For open-source OCR APIs, whereas there are not any direct utilization prices, we assess computational overhead by measuring reminiscence utilization throughout inference.

Benchmarking Outcomes

Because the datasets used—STROIE (totally different receipt pictures) and FUNSD (enterprise paperwork with tabular layouts)—comprise various structure types, the extracted textual content varies throughout fashions based mostly on their skill to protect construction. This variation impacts the Phrase Error Charge (WER) and Character Error Charge (CER), as these metrics depend upon the place of phrases and characters within the output.

A excessive error charge signifies {that a} mannequin struggles to take care of the chronological order of textual content, particularly in advanced layouts and tabular codecs.

1. Phrase Error Charge

WER of every mannequin on the FUNSD and STROIE datasets is offered beneath. These outcomes spotlight how nicely every mannequin preserves phrase order throughout totally different doc layouts.

2. Character Error Charge

CER of every mannequin on the FUNSD and STROIE datasets is offered beneath. These outcomes point out how precisely every mannequin captures character-level textual content whereas dealing with totally different doc layouts.

Why are the WER and CER metrics for Surya and Marker so excessive on the STROIE dataset?

STROIE’s intricate layouts make OCR tough. Surya tries to fill gaps by inserting additional phrases, resulting in excessive WER and CER, even after post-processing. Marker, which makes use of Surya for OCR and outputs markdown textual content, inherits these points. The markdown formatting additional misaligns textual content, worsening the error charges.

Variation in Mannequin Efficiency Throughout Datasets

OCR fashions carry out in a different way based mostly on dataset construction. Google Cloud Imaginative and prescient AI and Azure AI Doc Intelligence deal with various layouts higher, whereas open-source fashions like RapidOCR and Surya wrestle with structured codecs, resulting in extra errors.

Because the fashions wrestle to protect layouts, resulting in excessive WER and CER, we use one other metric—ROUGE Rating—to evaluate textual content similarity between the mannequin’s output and the bottom fact. In contrast to WER and CER, ROUGE focuses on content material similarity quite than phrase place. Because of this no matter structure preservation, a excessive ROUGE rating signifies that the extracted textual content carefully matches the reference, whereas a low rating suggests vital content material discrepancies.

3. ROUGE Rating

ROUGE Rating of every mannequin on the FUNSD and STROIE datasets is offered beneath. These outcomes mirror the content material similarity between the extracted textual content and the bottom fact, no matter structure preservation.

The ROUGE scores reveal that Google Cloud Imaginative and prescient AI constantly outperforms different fashions throughout each FUNSD (75.0%) and STROIE (87.8%), indicating superior textual content extraction. Surya and Marker, which depend on the identical backend, present comparable efficiency, although Marker barely lags on STROIE (70.3%). Extractous and Unstructured-IO rating the bottom in each datasets, suggesting weaker textual content coherence. PaddleOCR and Azure AI Doc Intelligence obtain balanced outcomes, making them aggressive options. The general development highlights the power of business APIs, whereas open-source fashions exhibit blended efficiency.

4. Latency per picture

Latency per picture for every mannequin is offered beneath. This measures the time taken by every mannequin to carry out OCR on one picture, offering insights into their effectivity and processing pace.

The latency evaluation exhibits that Google Cloud Imaginative and prescient AI, Amazon Textract, and Extractous preserve a great stability between pace and accuracy. Surya and Paddle OCR exhibit notably increased inference occasions, suggesting potential inefficiencies. Open-source fashions like Speedy OCR and Marker range in efficiency, with some providing aggressive speeds whereas others lag behind. Azure AI Doc Intelligence additionally exhibits average latency, making it a viable alternative relying on the use case.

5. Value or reminiscence utilization per picture

For business APIs, we current the utilization value (value per 1000 pictures processed). For open-source fashions, the metric signifies reminiscence consumption as a proxy for value, offering insights into their useful resource effectivity.

Amongst open-source fashions, Marker and Unstructured-IO have considerably increased reminiscence consumption, which can impression deployment in resource-constrained environments. Surya and Extractous strike a stability between efficiency and reminiscence effectivity. PaddleOCR and RapidOCR are probably the most light-weight choices, making them superb for low-memory eventualities.

Conclusion

Primarily based on the analysis throughout latency, inference time, and ROUGE rating, no single mannequin dominates in all elements. Nonetheless, some fashions stand out in particular areas:

• Greatest Latency & Inference Time: Extractous and Amazon Textract display the quickest response occasions, making them superb for real-time purposes.
• Greatest ROUGE Rating (Accuracy): Google Cloud Imaginative and prescient AI and Azure AI Doc Intelligence obtain the very best accuracy in textual content recognition, making them sturdy candidates for purposes requiring exact OCR.
• Greatest Reminiscence Effectivity: RapidOCR and PaddleOCR eat the least reminiscence, making them extremely appropriate for low-resource environments.

Greatest Mannequin Total

Contemplating a stability between accuracy, pace, and effectivity, Google Cloud Imaginative and prescient AI emerges as the perfect total performer. It supplies sturdy accuracy with aggressive inference time. Nonetheless, for open sourced fashions, PaddleOCR and RapidOCR provide the perfect trade-off between accuracy, pace and reminiscence effectivity.

Leaderboard of Greatest OCR APIs based mostly on totally different efficiency metrics:

LLM vs. Devoted OCR: A Case Examine

To grasp how OCR fashions evaluate to Giant Language Fashions (LLMs) in textual content extraction, we examined a difficult picture utilizing each LLaMa 3.2 11B Vision  and RapidOCR, a small however devoted OCR mannequin.

Outcomes:

1. LLaMa 3.2 11B Imaginative and prescient
   • Struggled with faint textual content, failing to reconstruct sure phrases.
   • Misinterpreted some characters and added hallucinated phrases.
   • Took considerably longer to course of the picture.
   • Used a number of compute assets.
2. RapidOCR
   • Precisely extracted many of the textual content regardless of the tough situations.
   • Ran effectively on very low compute assets.

Is OCR Nonetheless Related Right this moment?

With the rise of multimodal LLMs able to decoding pictures and textual content, some consider OCR could develop into out of date. Nonetheless, the fact is extra nuanced.

If you happen to or your finish clients should be 100% certain of information you are extracting from paperwork or pictures, OCR nonetheless is your greatest guess for now! Confidence scores and bounding packing containers from OCR APIs can be utilized to deduce when the output will not be dependable.

With LLMs you possibly can by no means be 100% certain of the veracity of the textual content output due to hallucinations and the insecurity scores.

Who Nonetheless Wants OCR?

• Enterprises Dealing with Excessive-Quantity Paperwork: Banks, authorized companies, and insurance coverage corporations depend on OCR for automated doc processing at scale.
• Governments and Compliance: Passport scanning, tax information, and regulatory filings nonetheless require OCR for structured extraction.
• AI-Powered Information Pipelines: Many companies combine OCR with NLP pipelines to transform paperwork into structured information earlier than making use of AI fashions.
• Multilingual and Low-Useful resource Language Purposes: OCR stays important for digitizing uncommon scripts the place LLMs lack coaching information.

Why Ought to Enterprises Nonetheless Care About OCR When Everybody Needs LLMs?

1. Accuracy and Reliability: LLMs generate hallucinations, whereas OCR ensures exact textual content extraction, making it important for authorized, monetary, and authorities purposes.
2. Velocity and Value Effectivity: OCR is light-weight and works on edge gadgets, whereas LLMs require excessive compute assets and cloud inference prices.
3. The long run will not be OCR vs. LLMs—it’s OCR and LLMs: OCR can extract clear textual content, and LLMs can then course of and interpret it for insights. AI-powered OCR fashions will proceed to enhance, integrating LLM reasoning for higher post-processing.

Closing Ideas

Whereas LLMs have expanded the probabilities of textual content extraction from pictures, OCR stays indispensable for structured, high-accuracy textual content retrieval and can all the time be essential for dependable doc processing. Fairly than changing OCR, LLMs will complement it, bringing higher understanding, context, and automation to extracted information.