GitHub30 / winocr

WinOCR

Installation

pip install winocr

Full install

pip install winocr[all]

Usage

Pillow

The language to be recognized can be specified by the lang parameter (second argument).

import winocr
from PIL import Image

img = Image.open('test.jpg')
(await winocr.recognize_pil(img, 'ja')).text

OpenCV

import winocr
import cv2

img = cv2.imread('test.jpg')
(await winocr.recognize_cv2(img, 'ja')).text

Connect to local runtime on Colaboratory

Create a local connection by following these instructions.

pip install jupyterlab jupyter_http_over_ws
jupyter serverextension enable --py jupyter_http_over_ws
jupyter notebook --NotebookApp.allow_origin='https://colab.research.google.com' --ip=0.0.0.0 --port=8888 --NotebookApp.port_retries=0

Also available on Jupyter / Jupyter Lab.

REPL

import cv2
from winocr import recognize_cv2_sync

img = cv2.imread('testocr.png')
recognize_cv2_sync(img)['text']
'This is a lot of 12 point text to test the ocr code and see if it works on all types of file format. The quick brown dog jumped over the lazy fox. The quick brown dog jumped over the lazy fox. The quick brown dog jumped over the lazy fox. The quick brown dog jumped over the lazy fox.'

from PIL import Image
from winocr import recognize_pil_sync

img = Image.open('testocr.png')
recognize_pil_sync(img)['text']
'This is a lot of 12 point text to test the ocr code and see if it works on all types of file format. The quick brown dog jumped over the lazy fox. The quick brown dog jumped over the lazy fox. The quick brown dog jumped over the lazy fox. The quick brown dog jumped over the lazy fox.'

Multi-Processing

from PIL import Image
import concurrent.futures
from winocr import recognize_pil_sync

images = [Image.open('testocr.png') for i in range(1000)]

with concurrent.futures.ProcessPoolExecutor() as executor:
  results = list(executor.map(recognize_pil_sync, images))
print(results)

Web API

Run server

pip install winocr[api]
winocr_serve

curl

curl localhost:8000?lang=ja --data-binary @test.jpg

Python

import requests

bytes = open('test.jpg', 'rb').read()
requests.post('http://localhost:8000/?lang=ja', bytes).json()['text']

You can run OCR with the Colaboratory runtime with ./ngrok http 8000

from PIL import Image
from io import BytesIO

img = Image.open('test.jpg')
# Preprocessing
buf = BytesIO()
img.save(buf, format='JPEG')
requests.post('https://15a5fabf0d78.ngrok.io/?lang=ja', buf.getvalue()).json()['text']

import cv2
import requests

img = cv2.imread('test.jpg')
# Preprocessing
requests.post('https://15a5fabf0d78.ngrok.io/?lang=ja', cv2.imencode('.jpg', img)[1].tobytes()).json()['text']

JavaScript

If you only need to recognize Chrome and English, you can also consider the Text Detection API.

// File
const file = document.querySelector('[type=file]').files[0]
await fetch('http://localhost:8000/', {method: 'POST', body: file}).then(r => r.json())

// Blob
const blob = await fetch('https://image.itmedia.co.jp/ait/articles/1706/15/news015_16.jpg').then(r=>r.blob())
await fetch('http://localhost:8000/?lang=ja', {method: 'POST', body: blob}).then(r => r.json())

It is also possible to run OCR Server on Windows Server.

Information that can be obtained

You can get angle, text, line, word, BoundingBox.

import pprint

result = await winocr.recognize_pil(img, 'ja')
pprint.pprint({
    'text_angle': result.text_angle,
    'text': result.text,
    'lines': [{
        'text': line.text,
        'words': [{
            'bounding_rect': {'x': word.bounding_rect.x, 'y': word.bounding_rect.y, 'width': word.bounding_rect.width, 'height': word.bounding_rect.height},
            'text': word.text
        } for word in line.words]
    } for line in result.lines]
})

Language installation

# Run as Administrator
Add-WindowsCapability -Online -Name "Language.OCR~~~en-US~0.0.1.0"
Add-WindowsCapability -Online -Name "Language.OCR~~~ja-JP~0.0.1.0"

# Search for installed languages
Get-WindowsCapability -Online -Name "Language.OCR*"
# State: Not Present language is not installed, so please install it if necessary.
Name         : Language.OCR~~~hu-HU~0.0.1.0
State        : NotPresent
DisplayName  : ハンガリー語の光学式文字認識
Description  : ハンガリー語の光学式文字認識
DownloadSize : 194407
InstallSize  : 535714

Name         : Language.OCR~~~it-IT~0.0.1.0
State        : NotPresent
DisplayName  : イタリア語の光学式文字認識
Description  : イタリア語の光学式文字認識
DownloadSize : 159875
InstallSize  : 485922

Name         : Language.OCR~~~ja-JP~0.0.1.0
State        : Installed
DisplayName  : 日本語の光学式文字認識
Description  : 日本語の光学式文字認識
DownloadSize : 1524589
InstallSize  : 3398536

Name         : Language.OCR~~~ko-KR~0.0.1.0
State        : NotPresent
DisplayName  : 韓国語の光学式文字認識
Description  : 韓国語の光学式文字認識
DownloadSize : 3405683
InstallSize  : 7890408

If you hate Python and just want to recognize it with PowerShell, click here

Multi-Processing

By processing in parallel, it is 3 times faster. You can make it even faster by increasing the number of cores!

from PIL import Image

images = [Image.open('testocr.png') for i in range(1000)]

1 core(elapsed 48s)

The CPU is not used up.

import winocr

[(await winocr.recognize_pil(img)).text for img in images]

4 cores(elapsed 16s)

I'm using 100% CPU.

Create a worker module.

%%writefile worker.py
import winocr
import asyncio

async def ensure_coroutine(awaitable):
    return await awaitable

def recognize_pil_text(img):
    return asyncio.run(ensure_coroutine(winocr.recognize_pil(img))).text

import worker
import concurrent.futures

with concurrent.futures.ProcessPoolExecutor() as executor:
  # https://stackoverflow.com/questions/62488423
  results = executor.map(worker.recognize_pil_text, images)
list(results)