270 lines
10 KiB
Markdown
270 lines
10 KiB
Markdown
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# PaddleOCR Quick Start
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[PaddleOCR Quick Start](#paddleocr-quick-start)
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* [1. Light Installation](#1-light-installation)
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+ [1.1 Install PaddlePaddle2.0](#11-install-paddlepaddle20)
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+ [1.2 Install PaddleOCR Whl Package](#12-install-paddleocr-whl-package)
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* [2. Easy-to-Use](#2-easy-to-use)
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+ [2.1 Use by command line](#21-use-by-command-line)
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- [2.1.1 English and Chinese Model](#211-english-and-chinese-model)
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- [2.1.2 Multi-language Model](#212-multi-language-model)
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- [2.1.3 LayoutParser](#213-layoutparser)
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+ [2.2 Use by Code](#22-use-by-code)
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- [2.2.1 Chinese & English Model and Multilingual Model](#221-chinese---english-model-and-multilingual-model)
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- [2.2.2 LayoutParser](#222-layoutparser)
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<a name="1-light-installation"></a>
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## 1. Light Installation
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<a name="11-install-paddlepaddle20"></a>
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### 1.1 Install PaddlePaddle2.0
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```bash
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# If you have cuda9 or cuda10 installed on your machine, please run the following command to install
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python3 -m pip install paddlepaddle-gpu==2.0.0 -i https://mirror.baidu.com/pypi/simple
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# If you only have cpu on your machine, please run the following command to install
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python3 -m pip install paddlepaddle==2.0.0 -i https://mirror.baidu.com/pypi/simple
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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/install/quick) for operation.
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<a name="12-install-paddleocr-whl-package"></a>
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### 1.2 Install PaddleOCR Whl Package
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```bash
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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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- **For layout analysis users**, run the following command to install **Layout-Parser**
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```bash
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pip3 install -U https://paddleocr.bj.bcebos.com/whl/layoutparser-0.0.0-py3-none-any.whl
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```
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<a name="2-easy-to-use"></a>
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## 2. Easy-to-Use
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<a name="21-use-by-command-line"></a>
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### 2.1 Use by command line
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PaddleOCR provides a series of test images, click xx to download, and then switch to the corresponding directory in the terminal
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```bash
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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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<a name="211-english-and-chinese-model"></a>
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#### 2.1.1 English and Chinese Model
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* Detection, direction classification and recognition: set the direction classifier parameter`--use_angle_cls true` to recognize vertical text.
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```bash
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paddleocr --image_dir ./imgs_en/img_12.jpg --use_angle_cls true --lang en
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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
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[[[442.0, 173.0], [1169.0, 173.0], [1169.0, 225.0], [442.0, 225.0]], ['ACKNOWLEDGEMENTS', 0.99283075]]
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[[[393.0, 340.0], [1207.0, 342.0], [1207.0, 389.0], [393.0, 387.0]], ['We would like to thank all the designers and', 0.9357758]]
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[[[399.0, 398.0], [1204.0, 398.0], [1204.0, 433.0], [399.0, 433.0]], ['contributors whohave been involved in the', 0.9592447]]
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......
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```
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* Only detection: set `--rec` to `false`
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```bash
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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
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[[756.0, 812.0], [805.0, 812.0], [805.0, 830.0], [756.0, 830.0]]
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[[820.0, 803.0], [1085.0, 801.0], [1085.0, 836.0], [820.0, 838.0]]
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[[393.0, 801.0], [715.0, 805.0], [715.0, 839.0], [393.0, 836.0]]
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......
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```
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* Only recognition: set `--det` to `false`
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```bash
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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
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['PAIN', 0.990372]
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```
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More whl package usage can be found in [whl package](./whl_en.md)
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<a name="212-multi-language-model"></a>
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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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<div align="center">
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<img src="../imgs_en/254.jpg" width="300" height="600">
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<img src="../imgs_results/multi_lang/img_02.jpg" width="600" height="600">
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</div>
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The result is a list, each item contains a text box, text and recognition confidence
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```text
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[('PHO CAPITAL', 0.95723116), [[66.0, 50.0], [327.0, 44.0], [327.0, 76.0], [67.0, 82.0]]]
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[('107 State Street', 0.96311164), [[72.0, 90.0], [451.0, 84.0], [452.0, 116.0], [73.0, 121.0]]]
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[('Montpelier Vermont', 0.97389287), [[69.0, 132.0], [501.0, 126.0], [501.0, 158.0], [70.0, 164.0]]]
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[('8022256183', 0.99810505), [[71.0, 175.0], [363.0, 170.0], [364.0, 202.0], [72.0, 207.0]]]
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[('REG 07-24-201706:59 PM', 0.93537045), [[73.0, 299.0], [653.0, 281.0], [654.0, 318.0], [74.0, 336.0]]]
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[('045555', 0.99346405), [[509.0, 331.0], [651.0, 325.0], [652.0, 356.0], [511.0, 362.0]]]
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[('CT1', 0.9988654), [[535.0, 367.0], [654.0, 367.0], [654.0, 406.0], [535.0, 406.0]]]
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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](./multi_languages_en.md)
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<a name="213-layoutparser"></a>
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#### 2.1.3 LayoutParser
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To use the layout analysis function of PaddleOCR, you need to specify `--type=structure`
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```bash
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paddleocr --image_dir=../doc/table/1.png --type=structure
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```
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- **Results Format**
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The returned results of PP-Structure is a list composed of a dict, an example is as follows
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```shell
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[
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{ 'type': 'Text',
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'bbox': [34, 432, 345, 462],
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'res': ([[36.0, 437.0, 341.0, 437.0, 341.0, 446.0, 36.0, 447.0], [41.0, 454.0, 125.0, 453.0, 125.0, 459.0, 41.0, 460.0]],
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[('Tigure-6. The performance of CNN and IPT models using difforen', 0.90060663), ('Tent ', 0.465441)])
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}
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]
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```
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The description of each field in dict is as follows
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| Parameter | Description |
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| --------- | ------------------------------------------------------------ |
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| type | Type of image area |
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| bbox | The coordinates of the image area in the original image, respectively [left upper x, left upper y, right bottom x, right bottom y] |
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| res | OCR or table recognition result of image area。<br> Table: HTML string of the table; <br> OCR: A tuple containing the detection coordinates and recognition results of each single line of text |
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- **Parameter Description：**
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| Parameter | Description | Default value |
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| --------------- | ------------------------------------------------------------ | -------------------------------------------- |
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| output | The path where excel and recognition results are saved | ./output/table |
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| table_max_len | The long side of the image is resized in table structure model | 488 |
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| table_model_dir | inference model path of table structure model | None |
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| table_char_type | dict path of table structure model | ../ppocr/utils/dict/table_structure_dict.txt |
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<a name="22-use-by-code"></a>
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### 2.2 Use by Code
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<a name="221-chinese---english-model-and-multilingual-model"></a>
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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
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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 line in result:
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print(line)
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# draw result
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from PIL import Image
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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
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[[[442.0, 173.0], [1169.0, 173.0], [1169.0, 225.0], [442.0, 225.0]], ['ACKNOWLEDGEMENTS', 0.99283075]]
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[[[393.0, 340.0], [1207.0, 342.0], [1207.0, 389.0], [393.0, 387.0]], ['We would like to thank all the designers and', 0.9357758]]
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[[[399.0, 398.0], [1204.0, 398.0], [1204.0, 433.0], [399.0, 433.0]], ['contributors whohave been involved in the', 0.9592447]]
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......
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```
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Visualization of results
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<div align="center">
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<img src="../imgs_results/whl/12_det_rec.jpg" width="800">
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</div>
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<a name="222-layoutparser"></a>
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#### 2.2.2 LayoutParser
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```python
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import os
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import cv2
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from paddleocr import PPStructure,draw_structure_result,save_structure_res
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table_engine = PPStructure(show_log=True)
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save_folder = './output/table'
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img_path = './table/1.png'
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img = cv2.imread(img_path)
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result = table_engine(img)
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save_structure_res(result, save_folder,os.path.basename(img_path).split('.')[0])
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for line in result:
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line.pop('img')
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print(line)
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from PIL import Image
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font_path = './fonts/simfang.ttf'
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image = Image.open(img_path).convert('RGB')
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im_show = draw_structure_result(image, result,font_path=font_path)
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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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