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287 lines
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Markdown
287 lines
12 KiB
Markdown
---
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comments: true
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---
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# PaddleOCR Quick Start
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**Note:** This tutorial mainly introduces the usage of PP-OCR series models, please refer to [PP-Structure Quick Start](../ppstructure/overview.en.md) for the quick use of document analysis related functions.
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## 1. Installation
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### 1.1 Install PaddlePaddle
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> If you do not have a Python environment, please refer to [Environment Preparation](./environment.en.md).
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- If you have CUDA 11 installed on your machine, please run the following command to install
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```bash linenums="1"
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pip install paddlepaddle-gpu
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```
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- If you have no available GPU on your machine, please run the following command to install the CPU version
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```bash linenums="1"
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python -m pip install paddlepaddle
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```
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For more software version requirements, please refer to the instructions in [Installation Document](https://www.paddlepaddle.org.cn/en/install/quick) for operation.
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### 1.2 Install PaddleOCR Whl Package
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```bash linenums="1"
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pip install "paddleocr>=2.0.1" # Recommend to use version 2.0.1+
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```
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- **For windows users:** If you getting this error `OSError: [WinError 126] The specified module could not be found` when you install shapely on windows. Please try to download Shapely whl file [here](http://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely).
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Reference: [Solve shapely installation on windows](https://stackoverflow.com/questions/44398265/install-shapely-oserror-winerror-126-the-specified-module-could-not-be-found)
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## 2. Easy-to-Use
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### 2.1 Use by Command Line
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PaddleOCR provides a series of test images, click [here](https://paddleocr.bj.bcebos.com/dygraph_v2.1/ppocr_img.zip) to download, and then switch to the corresponding directory in the terminal
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```bash linenums="1"
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cd /path/to/ppocr_img
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```
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If you do not use the provided test image, you can replace the following `--image_dir` parameter with the corresponding test image path
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#### 2.1.1 Chinese and English Model
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- Detection, direction classification and recognition: set the parameter`--use_gpu false` to disable the gpu device
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```bash linenums="1"
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paddleocr --image_dir ./imgs_en/img_12.jpg --use_angle_cls true --lang en --use_gpu false
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```
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Output will be a list, each item contains bounding box, text and recognition confidence
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```bash linenums="1"
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[[[441.0, 174.0], [1166.0, 176.0], [1165.0, 222.0], [441.0, 221.0]], ('ACKNOWLEDGEMENTS', 0.9971134662628174)]
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[[[403.0, 346.0], [1204.0, 348.0], [1204.0, 384.0], [402.0, 383.0]], ('We would like to thank all the designers and', 0.9761400818824768)]
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[[[403.0, 396.0], [1204.0, 398.0], [1204.0, 434.0], [402.0, 433.0]], ('contributors who have been involved in the', 0.9791957139968872)]
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......
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```
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pdf file is also supported, you can infer the first few pages by using the `page_num` parameter, the default is 0, which means infer all pages
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```bash linenums="1"
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paddleocr --image_dir ./xxx.pdf --use_angle_cls true --use_gpu false --page_num 2
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```
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- Only detection: set `--rec` to `false`
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```bash linenums="1"
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paddleocr --image_dir ./imgs_en/img_12.jpg --rec false
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```
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Output will be a list, each item only contains bounding box
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```bash linenums="1"
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[[397.0, 802.0], [1092.0, 802.0], [1092.0, 841.0], [397.0, 841.0]]
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[[397.0, 750.0], [1211.0, 750.0], [1211.0, 789.0], [397.0, 789.0]]
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[[397.0, 702.0], [1209.0, 698.0], [1209.0, 734.0], [397.0, 738.0]]
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......
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```
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- Only recognition: set `--det` to `false`
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```bash linenums="1"
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paddleocr --image_dir ./imgs_words_en/word_10.png --det false --lang en
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```
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Output will be a list, each item contains text and recognition confidence
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```bash linenums="1"
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['PAIN', 0.9934559464454651]
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```
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**Version**
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paddleocr uses the PP-OCRv4 model by default(`--ocr_version PP-OCRv4`). If you want to use other versions, you can set the parameter `--ocr_version`, the specific version description is as follows:
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| version name | description |
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| --- | --- |
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| PP-OCRv4 | support Chinese and English detection and recognition, direction classifier, support multilingual recognition |
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| PP-OCRv3 | support Chinese and English detection and recognition, direction classifier, support multilingual recognition |
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| PP-OCRv2 | only supports Chinese and English detection and recognition, direction classifier, multilingual model is not updated |
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| PP-OCR | support Chinese and English detection and recognition, direction classifier, support multilingual recognition |
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If you want to add your own trained model, you can add model links and keys in [paddleocr](https://github.com/PaddlePaddle/PaddleOCR/blob/c65a66c5fd37dee64916a8b2a2c84ea273d98cac/paddleocr.py) and recompile.
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More whl package usage can be found in [whl package](./blog/whl.en.md)
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#### 2.1.2 Multi-language Model
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PaddleOCR currently supports 80 languages, which can be switched by modifying the `--lang` parameter.
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``` bash
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paddleocr --image_dir ./doc/imgs_en/254.jpg --lang=en
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```
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The result is a list, each item contains a text box, text and recognition confidence
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```text linenums="1"
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[[[67.0, 51.0], [327.0, 46.0], [327.0, 74.0], [68.0, 80.0]], ('PHOCAPITAL', 0.9944712519645691)]
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[[[72.0, 92.0], [453.0, 84.0], [454.0, 114.0], [73.0, 122.0]], ('107 State Street', 0.9744491577148438)]
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[[[69.0, 135.0], [501.0, 125.0], [501.0, 156.0], [70.0, 165.0]], ('Montpelier Vermont', 0.9357033967971802)]
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......
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```
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Commonly used multilingual abbreviations include
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| Language | Abbreviation | | Language | Abbreviation | | Language | Abbreviation |
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| ------------------- | ------------ | ---- | -------- | ------------ | ---- | -------- | ------------ |
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| Chinese & English | ch | | French | fr | | Japanese | japan |
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| English | en | | German | german | | Korean | korean |
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| Chinese Traditional | chinese_cht | | Italian | it | | Russian | ru |
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A list of all languages and their corresponding abbreviations can be found in [Multi-Language Model Tutorial](./blog/multi_languages.en.md)
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### 2.2 Use by Code
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#### 2.2.1 Chinese & English Model and Multilingual Model
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- detection, angle classification and recognition:
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```python linenums="1"
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from paddleocr import PaddleOCR,draw_ocr
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# Paddleocr supports Chinese, English, French, German, Korean and Japanese.
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# You can set the parameter `lang` as `ch`, `en`, `fr`, `german`, `korean`, `japan`
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# to switch the language model in order.
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ocr = PaddleOCR(use_angle_cls=True, lang='en') # need to run only once to download and load model into memory
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img_path = './imgs_en/img_12.jpg'
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result = ocr.ocr(img_path, cls=True)
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for idx in range(len(result)):
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res = result[idx]
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for line in res:
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print(line)
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# draw result
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from PIL import Image
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result = result[0]
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image = Image.open(img_path).convert('RGB')
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boxes = [line[0] for line in result]
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txts = [line[1][0] for line in result]
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scores = [line[1][1] for line in result]
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im_show = draw_ocr(image, boxes, txts, scores, font_path='./fonts/simfang.ttf')
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im_show = Image.fromarray(im_show)
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im_show.save('result.jpg')
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```
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Output will be a list, each item contains bounding box, text and recognition confidence
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```bash linenums="1"
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[[[441.0, 174.0], [1166.0, 176.0], [1165.0, 222.0], [441.0, 221.0]], ('ACKNOWLEDGEMENTS', 0.9971134662628174)]
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[[[403.0, 346.0], [1204.0, 348.0], [1204.0, 384.0], [402.0, 383.0]], ('We would like to thank all the designers and', 0.9761400818824768)]
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[[[403.0, 396.0], [1204.0, 398.0], [1204.0, 434.0], [402.0, 433.0]], ('contributors who have been involved in the', 0.9791957139968872)]
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......
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```
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Visualization of results
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If the input is a PDF file, you can refer to the following code for visualization
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```python linenums="1"
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from paddleocr import PaddleOCR, draw_ocr
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# Paddleocr supports Chinese, English, French, German, Korean and Japanese.
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# You can set the parameter `lang` as `ch`, `en`, `fr`, `german`, `korean`, `japan`
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# to switch the language model in order.
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PAGE_NUM = 10 # Set the recognition page number
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pdf_path = 'default.pdf'
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ocr = PaddleOCR(use_angle_cls=True, lang="ch", page_num=PAGE_NUM) # need to run only once to download and load model into memory
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# ocr = PaddleOCR(use_angle_cls=True, lang="ch", page_num=PAGE_NUM,use_gpu=0) # To Use GPU,uncomment this line and comment the above one.
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result = ocr.ocr(pdf_path, cls=True)
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for idx in range(len(result)):
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res = result[idx]
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if res == None: # Skip when empty result detected to avoid TypeError:NoneType
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print(f"[DEBUG] Empty page {idx+1} detected, skip it.")
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continue
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for line in res:
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print(line)
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# draw the result
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import fitz
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from PIL import Image
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import cv2
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import numpy as np
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imgs = []
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with fitz.open(pdf_path) as pdf:
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for pg in range(0, PAGE_NUM):
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page = pdf[pg]
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mat = fitz.Matrix(2, 2)
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pm = page.get_pixmap(matrix=mat, alpha=False)
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# if width or height > 2000 pixels, don't enlarge the image
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if pm.width > 2000 or pm.height > 2000:
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pm = page.get_pixmap(matrix=fitz.Matrix(1, 1), alpha=False)
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img = Image.frombytes("RGB", [pm.width, pm.height], pm.samples)
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img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
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imgs.append(img)
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for idx in range(len(result)):
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res = result[idx]
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if res == None:
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continue
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image = imgs[idx]
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boxes = [line[0] for line in res]
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txts = [line[1][0] for line in res]
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scores = [line[1][1] for line in res]
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im_show = draw_ocr(image, boxes, txts, scores, font_path='doc/fonts/simfang.ttf')
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im_show = Image.fromarray(im_show)
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im_show.save('result_page_{}.jpg'.format(idx))
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```
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- Detection and Recognition Using Sliding Windows
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To perform OCR using sliding windows, the following code snippet can be employed:
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```python linenums="1"
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from paddleocr import PaddleOCR
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from PIL import Image, ImageDraw, ImageFont
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# Initialize OCR engine
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ocr = PaddleOCR(use_angle_cls=True, lang="en")
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img_path = "./very_large_image.jpg"
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slice = {'horizontal_stride': 300, 'vertical_stride': 500, 'merge_x_thres': 50, 'merge_y_thres': 35}
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results = ocr.ocr(img_path, cls=True, slice=slice)
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# Load image
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image = Image.open(img_path).convert("RGB")
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draw = ImageDraw.Draw(image)
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font = ImageFont.truetype("./doc/fonts/simfang.ttf", size=20) # Adjust size as needed
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# Process and draw results
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for res in results:
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for line in res:
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box = [tuple(point) for point in line[0]]
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# Finding the bounding box
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box = [(min(point[0] for point in box), min(point[1] for point in box)),
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(max(point[0] for point in box), max(point[1] for point in box))]
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txt = line[1][0]
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draw.rectangle(box, outline="red", width=2) # Draw rectangle
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draw.text((box[0][0], box[0][1] - 25), txt, fill="blue", font=font) # Draw text above the box
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# Save result
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image.save("result.jpg")
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```
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This example initializes the PaddleOCR instance with angle classification enabled and sets the language to English. The `ocr` method is then called with several parameters to customize the detection and recognition process, including the `slice` parameter for handling image slices.
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For a more comprehensive understanding of the slicing operation, please refer to the [slice operation documentation](./blog/slice.en.md).
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## 3. Summary
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In this section, you have mastered the use of PaddleOCR whl package.
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PaddleX provides a high-quality ecological model of the paddle. It is a one-stop full-process high-efficiency development platform for training, pressing and pushing. Its mission is to help AI technology to be implemented quickly. The vision is to make everyone an AI Developer! Currently PP-OCRv4 has been launched on PaddleX, you can enter [General OCR](https://aistudio.baidu.com/aistudio/modelsdetail?modelId=286) to experience the whole process of model training, compression and inference deployment.
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