paddleocr 0.0.2

Awesome OCR toolkits based on PaddlePaddle （8.6M ultra-lightweight pre-trained model, support training and deployment among server, mobile, embeded and IoT devices

ppcor package

Get started quickly

install package

install by pypi

pip install paddleocr

build own whl package and install

python setup.py bdist_wheel
pip install dist/paddleocr-0.0.1-py3-none-any.whl

1. Use by code

detection and recognition

from paddleocr import PaddleOCR
ocr = PaddleOCR(model_storage_directory='./model') # need to run only once to load model into memory
img = 'PaddleOCR/doc/imgs/11.jpg'
result = ocr.ocr(img)
for line in result:
    print(line)

Output will be a list, each item contains bounding box, text and recognition confidence

[[[24.0, 36.0], [304.0, 34.0], [304.0, 72.0], [24.0, 74.0]], ['纯臻营养护发素', 0.964739]]
[[[24.0, 80.0], [172.0, 80.0], [172.0, 104.0], [24.0, 104.0]], ['产品信息/参数', 0.98069626]]
[[[24.0, 109.0], [333.0, 109.0], [333.0, 136.0], [24.0, 136.0]], ['（45元/每公斤，100公斤起订）', 0.9676722]]
[[[22.0, 140.0], [284.0, 140.0], [284.0, 167.0], [22.0, 167.0]], ['每瓶22元，1000瓶起订）', 0.97444016]]
[[[22.0, 174.0], [85.0, 174.0], [85.0, 198.0], [22.0, 198.0]], ['【品牌】', 0.8187138]]
[[[89.0, 176.0], [301.0, 176.0], [301.0, 196.0], [89.0, 196.0]], ['：代加工方式/OEMODM', 0.9421848]]
[[[23.0, 205.0], [85.0, 205.0], [85.0, 229.0], [23.0, 229.0]], ['【品名】', 0.76008326]]
[[[88.0, 204.0], [235.0, 206.0], [235.0, 229.0], [88.0, 227.0]], ['：纯臻营养护发素', 0.9633639]]
[[[23.0, 236.0], [121.0, 236.0], [121.0, 261.0], [23.0, 261.0]], ['【产品编号】', 0.84101385]]
[[[110.0, 239.0], [239.0, 239.0], [239.0, 256.0], [110.0, 256.0]], ['1：YM-X-3011', 0.8621878]]
[[[414.0, 233.0], [430.0, 233.0], [430.0, 304.0], [414.0, 304.0]], ['ODM OEM', 0.9084018]]
[[[23.0, 268.0], [183.0, 268.0], [183.0, 292.0], [23.0, 292.0]], ['【净含量】：220ml', 0.9278281]]
[[[24.0, 301.0], [118.0, 301.0], [118.0, 321.0], [24.0, 321.0]], ['【适用人群】', 0.90901047]]
[[[127.0, 300.0], [254.0, 300.0], [254.0, 323.0], [127.0, 323.0]], ['：适合所有肤质', 0.95465785]]
[[[24.0, 332.0], [117.0, 332.0], [117.0, 353.0], [24.0, 353.0]], ['【主要成分】', 0.88936955]]
[[[139.0, 332.0], [236.0, 332.0], [236.0, 352.0], [139.0, 352.0]], ['鲸蜡硬脂醇', 0.9447544]]
[[[248.0, 332.0], [345.0, 332.0], [345.0, 352.0], [248.0, 352.0]], ['燕麦B-葡聚', 0.89748293]]
[[[54.0, 363.0], [232.0, 363.0], [232.0, 383.0], [54.0, 383.0]], [' 椰油酰胺丙基甜菜碱', 0.902023]]
[[[25.0, 364.0], [64.0, 364.0], [64.0, 383.0], [25.0, 383.0]], ['糖、', 0.985203]]
[[[244.0, 363.0], [281.0, 363.0], [281.0, 382.0], [244.0, 382.0]], ['泛服', 0.44537082]]
[[[367.0, 367.0], [475.0, 367.0], [475.0, 388.0], [367.0, 388.0]], ['（成品包材）', 0.9834532]]
[[[24.0, 395.0], [120.0, 395.0], [120.0, 416.0], [24.0, 416.0]], ['【主要功能】', 0.88684446]]
[[[128.0, 397.0], [273.0, 397.0], [273.0, 414.0], [128.0, 414.0]], ['：可紧致头发磷层', 0.9342501]]
[[[265.0, 395.0], [361.0, 395.0], [361.0, 415.0], [265.0, 415.0]], ['琴，从而达到', 0.8253762]]
[[[25.0, 425.0], [372.0, 425.0], [372.0, 448.0], [25.0, 448.0]], ['即时持久改善头发光泽的效果，给干燥的头', 0.97785276]]
[[[26.0, 457.0], [137.0, 457.0], [137.0, 477.0], [26.0, 477.0]], ['发足够的滋养', 0.9577897]]

only detection

from paddleocr import PaddleOCR
ocr = PaddleOCR(model_storage_directory='./model') # need to run only once to load model into memory
img = 'PaddleOCR/doc/imgs/11.jpg'
result = ocr.ocr(img,rec=False)
for line in result:
    print(line)

Output will be a list, each item only contains bounding box

[[26.0, 457.0], [137.0, 457.0], [137.0, 477.0], [26.0, 477.0]]
[[25.0, 425.0], [372.0, 425.0], [372.0, 448.0], [25.0, 448.0]]
[[128.0, 397.0], [273.0, 397.0], [273.0, 414.0], [128.0, 414.0]]
[[265.0, 395.0], [361.0, 395.0], [361.0, 415.0], [265.0, 415.0]]
[[24.0, 395.0], [120.0, 395.0], [120.0, 416.0], [24.0, 416.0]]
[[367.0, 367.0], [475.0, 367.0], [475.0, 388.0], [367.0, 388.0]]
[[54.0, 363.0], [232.0, 363.0], [232.0, 383.0], [54.0, 383.0]]
[[25.0, 364.0], [64.0, 364.0], [64.0, 383.0], [25.0, 383.0]]
[[244.0, 363.0], [281.0, 363.0], [281.0, 382.0], [244.0, 382.0]]
[[248.0, 332.0], [345.0, 332.0], [345.0, 352.0], [248.0, 352.0]]
[[139.0, 332.0], [236.0, 332.0], [236.0, 352.0], [139.0, 352.0]]
[[24.0, 332.0], [117.0, 332.0], [117.0, 353.0], [24.0, 353.0]]
[[127.0, 300.0], [254.0, 300.0], [254.0, 323.0], [127.0, 323.0]]
[[24.0, 301.0], [118.0, 301.0], [118.0, 321.0], [24.0, 321.0]]
[[23.0, 268.0], [183.0, 268.0], [183.0, 292.0], [23.0, 292.0]]
[[110.0, 239.0], [239.0, 239.0], [239.0, 256.0], [110.0, 256.0]]
[[23.0, 236.0], [121.0, 236.0], [121.0, 261.0], [23.0, 261.0]]
[[414.0, 233.0], [430.0, 233.0], [430.0, 304.0], [414.0, 304.0]]
[[88.0, 204.0], [235.0, 206.0], [235.0, 229.0], [88.0, 227.0]]
[[23.0, 205.0], [85.0, 205.0], [85.0, 229.0], [23.0, 229.0]]
[[89.0, 176.0], [301.0, 176.0], [301.0, 196.0], [89.0, 196.0]]
[[22.0, 174.0], [85.0, 174.0], [85.0, 198.0], [22.0, 198.0]]
[[22.0, 140.0], [284.0, 140.0], [284.0, 167.0], [22.0, 167.0]]
[[24.0, 109.0], [333.0, 109.0], [333.0, 136.0], [24.0, 136.0]]
[[24.0, 80.0], [172.0, 80.0], [172.0, 104.0], [24.0, 104.0]]
[[24.0, 36.0], [304.0, 34.0], [304.0, 72.0], [24.0, 74.0]]

only recognition

from paddleocr import PaddleOCR
ocr = PaddleOCR(model_storage_directory='./model') # need to run only once to load model into memory
img = 'PaddleOCR/doc/imgs_words/ch/word_1.jpg'
result = ocr.ocr(img,det=False)
for line in result:
    print(line)

Output will be a list, each item contains text and recognition confidence

['韩国小馆', 0.9907421]

Use by command line

show help information

paddleocr -h

detection and recognition

paddleocr --image_dir PaddleOCR/doc/imgs/11.jpg

Output will be a list, each item contains bounding box, text and recognition confidence

[[[24.0, 36.0], [304.0, 34.0], [304.0, 72.0], [24.0, 74.0]], ['纯臻营养护发素', 0.964739]]
[[[24.0, 80.0], [172.0, 80.0], [172.0, 104.0], [24.0, 104.0]], ['产品信息/参数', 0.98069626]]
[[[24.0, 109.0], [333.0, 109.0], [333.0, 136.0], [24.0, 136.0]], ['（45元/每公斤，100公斤起订）', 0.9676722]]
[[[22.0, 140.0], [284.0, 140.0], [284.0, 167.0], [22.0, 167.0]], ['每瓶22元，1000瓶起订）', 0.97444016]]
[[[22.0, 174.0], [85.0, 174.0], [85.0, 198.0], [22.0, 198.0]], ['【品牌】', 0.8187138]]
[[[89.0, 176.0], [301.0, 176.0], [301.0, 196.0], [89.0, 196.0]], ['：代加工方式/OEMODM', 0.9421848]]
[[[23.0, 205.0], [85.0, 205.0], [85.0, 229.0], [23.0, 229.0]], ['【品名】', 0.76008326]]
[[[88.0, 204.0], [235.0, 206.0], [235.0, 229.0], [88.0, 227.0]], ['：纯臻营养护发素', 0.9633639]]
[[[23.0, 236.0], [121.0, 236.0], [121.0, 261.0], [23.0, 261.0]], ['【产品编号】', 0.84101385]]
[[[110.0, 239.0], [239.0, 239.0], [239.0, 256.0], [110.0, 256.0]], ['1：YM-X-3011', 0.8621878]]
[[[414.0, 233.0], [430.0, 233.0], [430.0, 304.0], [414.0, 304.0]], ['ODM OEM', 0.9084018]]
[[[23.0, 268.0], [183.0, 268.0], [183.0, 292.0], [23.0, 292.0]], ['【净含量】：220ml', 0.9278281]]
[[[24.0, 301.0], [118.0, 301.0], [118.0, 321.0], [24.0, 321.0]], ['【适用人群】', 0.90901047]]
[[[127.0, 300.0], [254.0, 300.0], [254.0, 323.0], [127.0, 323.0]], ['：适合所有肤质', 0.95465785]]
[[[24.0, 332.0], [117.0, 332.0], [117.0, 353.0], [24.0, 353.0]], ['【主要成分】', 0.88936955]]
[[[139.0, 332.0], [236.0, 332.0], [236.0, 352.0], [139.0, 352.0]], ['鲸蜡硬脂醇', 0.9447544]]
[[[248.0, 332.0], [345.0, 332.0], [345.0, 352.0], [248.0, 352.0]], ['燕麦B-葡聚', 0.89748293]]
[[[54.0, 363.0], [232.0, 363.0], [232.0, 383.0], [54.0, 383.0]], [' 椰油酰胺丙基甜菜碱', 0.902023]]
[[[25.0, 364.0], [64.0, 364.0], [64.0, 383.0], [25.0, 383.0]], ['糖、', 0.985203]]
[[[244.0, 363.0], [281.0, 363.0], [281.0, 382.0], [244.0, 382.0]], ['泛服', 0.44537082]]
[[[367.0, 367.0], [475.0, 367.0], [475.0, 388.0], [367.0, 388.0]], ['（成品包材）', 0.9834532]]
[[[24.0, 395.0], [120.0, 395.0], [120.0, 416.0], [24.0, 416.0]], ['【主要功能】', 0.88684446]]
[[[128.0, 397.0], [273.0, 397.0], [273.0, 414.0], [128.0, 414.0]], ['：可紧致头发磷层', 0.9342501]]
[[[265.0, 395.0], [361.0, 395.0], [361.0, 415.0], [265.0, 415.0]], ['琴，从而达到', 0.8253762]]
[[[25.0, 425.0], [372.0, 425.0], [372.0, 448.0], [25.0, 448.0]], ['即时持久改善头发光泽的效果，给干燥的头', 0.97785276]]
[[[26.0, 457.0], [137.0, 457.0], [137.0, 477.0], [26.0, 477.0]], ['发足够的滋养', 0.9577897]]

only detection

paddleocr --image_dir PaddleOCR/doc/imgs_words/ch/word_1.jpg --rec false

Output will be a list, each item only contains bounding box

[[26.0, 457.0], [137.0, 457.0], [137.0, 477.0], [26.0, 477.0]]
[[25.0, 425.0], [372.0, 425.0], [372.0, 448.0], [25.0, 448.0]]
[[128.0, 397.0], [273.0, 397.0], [273.0, 414.0], [128.0, 414.0]]
[[265.0, 395.0], [361.0, 395.0], [361.0, 415.0], [265.0, 415.0]]
[[24.0, 395.0], [120.0, 395.0], [120.0, 416.0], [24.0, 416.0]]
[[367.0, 367.0], [475.0, 367.0], [475.0, 388.0], [367.0, 388.0]]
[[54.0, 363.0], [232.0, 363.0], [232.0, 383.0], [54.0, 383.0]]
[[25.0, 364.0], [64.0, 364.0], [64.0, 383.0], [25.0, 383.0]]
[[244.0, 363.0], [281.0, 363.0], [281.0, 382.0], [244.0, 382.0]]
[[248.0, 332.0], [345.0, 332.0], [345.0, 352.0], [248.0, 352.0]]
[[139.0, 332.0], [236.0, 332.0], [236.0, 352.0], [139.0, 352.0]]
[[24.0, 332.0], [117.0, 332.0], [117.0, 353.0], [24.0, 353.0]]
[[127.0, 300.0], [254.0, 300.0], [254.0, 323.0], [127.0, 323.0]]
[[24.0, 301.0], [118.0, 301.0], [118.0, 321.0], [24.0, 321.0]]
[[23.0, 268.0], [183.0, 268.0], [183.0, 292.0], [23.0, 292.0]]
[[110.0, 239.0], [239.0, 239.0], [239.0, 256.0], [110.0, 256.0]]
[[23.0, 236.0], [121.0, 236.0], [121.0, 261.0], [23.0, 261.0]]
[[414.0, 233.0], [430.0, 233.0], [430.0, 304.0], [414.0, 304.0]]
[[88.0, 204.0], [235.0, 206.0], [235.0, 229.0], [88.0, 227.0]]
[[23.0, 205.0], [85.0, 205.0], [85.0, 229.0], [23.0, 229.0]]
[[89.0, 176.0], [301.0, 176.0], [301.0, 196.0], [89.0, 196.0]]
[[22.0, 174.0], [85.0, 174.0], [85.0, 198.0], [22.0, 198.0]]
[[22.0, 140.0], [284.0, 140.0], [284.0, 167.0], [22.0, 167.0]]
[[24.0, 109.0], [333.0, 109.0], [333.0, 136.0], [24.0, 136.0]]
[[24.0, 80.0], [172.0, 80.0], [172.0, 104.0], [24.0, 104.0]]
[[24.0, 36.0], [304.0, 34.0], [304.0, 72.0], [24.0, 74.0]]

only recognition

paddleocr --image_dir PaddleOCR/doc/imgs_words/ch/word_1.jpg --det false

Output will be a list, each item contains text and recognition confidence

['韩国小馆', 0.9907421]

Parameter Description

Parameter	Description	Default value
use_gpu	use GPU or not	TRUE
gpu_mem	GPU memory size used for initialization	8000M
image_dir	The images path or folder path for predicting when used by the command line
det_algorithm	Type of detection algorithm selected	DB
det_model_name	There are two ways to use: 1. The name of the detection algorithm which must be in the support list(only ch_det_mv3_db is built in currently), and the supported list will be displayed when the wrong parameter is passed in. 2. The path of the inference model that has been converted by yourself. At this time, the model path must contains model and params files. When choosing this method, you need to give the name of det_algorithm	ch_det_mv3_db
det_max_side_len	The maximum size of the long side of the image. When the long side exceeds this value, the long side will be resized to this size, and the short side will be scaled proportionally	960
det_db_thresh	Binarization threshold value of DB output map	0.3
det_db_box_thresh	The threshold value of the DB output box. Boxes score lower than this value will be discarded	0.5
det_db_unclip_ratio	The expanded ratio of DB output box	2
det_east_score_thresh	Binarization threshold value of EAST output map	0.8
det_east_cover_thresh	The threshold value of the EAST output box. Boxes score lower than this value will be discarded	0.1
det_east_nms_thresh	The NMS threshold value of EAST model output box	0.2
rec_algorithm	Type of recognition algorithm selected	CRNN
rec_model_name	There are two ways to use: 1. The name of the recognition algorithm which must be in the support list(only supports CRNN, Rosetta, STAR, RARE and other algorithms currently, but only ch_rec_mv3_crnn_enhance is built-in), and the supported list will be displayed when the wrong parameter is passed in. 2. The path of the inference model that has been converted by yourself. At this time, the model path must contains model and params files. When choosing this method, you need to give the name of rec_algorithm	ch_rec_mv3_crnn_enhance
rec_image_shape	image shape of recognition algorithm	"3,32,320"
rec_char_type	Character type of recognition algorithm, Chinese (ch) or English (en)	ch
rec_batch_num	When performing recognition, the batchsize of forward images	30
rec_char_dict_path	the alphabet path which needs to be modified to your own path when rec_model_Name use mode 2
use_space_char	Whether to recognize spaces	TRUE
enable_mkldnn	Whether to enable mkldnn	FALSE
model_storage_directory	Download model save path when det_model_name or rec_model_name use mode 1	～/.ppocr
det	Enable detction when ppocr.ocr func exec	TRUE
rec	Enable detction when ppocr.ocr func exec	TRUE