rapidocr

det_model_path = 'models/ch_ppocr_mobile_v2_det_train.onnx'
cls_model_path = 'models/ch_ppocr_mobile_v2.0_cls_infer.onnx'
rec_model_path = 'models/ch_ppocr_mobile_v2.0_rec_pre_infer.onnx'
image_path = r'test_images\long1.jpg'

text_sys = TextSystem(det_model_path, rec_model_path,
                        use_angle_cls=True,
                        cls_model_path=cls_model_path)
dt_boxes, rec_res = text_sys(image_path)
visualize(image_path, dt_boxes, rec_res)

通用超轻量文本检测模型 ch_ppocr_mobile_v2_det_train

Note: 支持任意尺寸输入
server版文本检测只是采用了ResNet18作为backbone,其他与超轻量无区别
详细见ch_ppocr_mobile_v2_det/text_detect.py

简单示例

det_model_path = 'models/ch_ppocr_mobile_v2_det_train.onnx'
image_path = r'test_images/det_images/1.jpg'

test_detector = TextDetector(det_model_path)

img, flag = check_and_read_gif(image_path)
if not flag:
    img = cv2.imread(image_path)
if img is None:
    raise ValueError(f"error in loading image:{image_path}")

# dst_boxes: 检测到图像中的文本框坐标，ndarray格式
# (10, 4, 2)→[10个，4个坐标，每个坐标两个点]
dt_boxes, elapse = test_detector(img)
print(dt_boxes.shape)
print(elapse)

通用超轻量方向分类器模型 ch_ppocr_mobile_v2_cls

Note: 支持0 180两个方向分类
详细见ch_ppocr_mobile_v2_cls/text_cls.py

简单示例

text_classifier = TextClassifier(args.cls_model_dir)

img_list, cls_res, predict_time = text_classifier(args.image_dir)
for ino in range(len(img_list)):
    print(f"分类结果:{cls_res[ino]}")

通用超轻量文本识别模型 ch_ppocr_mobile_v2_rec

Note: 支持任意尺寸输入
详细见ch_ppocr_mobile_v2_rec/text_recognize.py

简单示例：

rec_model_path = r'models/ch_ppocr_mobile_v2.0_rec_pre_infer.onnx'
image_path = r'test_images/rec_images/2021-01-19_13-44-34.png'
text_recongnizer = TextRecognizer(rec_model_path)

rec_res, elapse = text_recongnizer(image_path)
print(f'识别结果：{rec_res}/tcost: {elapse}s')

通用超轻量文本检测模型 ch_ppocr_mobile_v1.1_det

Note: 支持任意尺寸输入
详细见ch_ppocr_mobile_v1_det/text_detect.py

简单示例

text_detector = TextDetector(args.model_path)

img, flag = check_and_read_gif(args.image_path)
if not flag:
    img = cv2.imread(args.image_path)
if img is None:
    raise ValueError(f"error in loading image:{args.image_path}")

dt_boxes, elapse = text_detector(img)

plot_img = draw_text_det_res(dt_boxes, img)
cv2.imwrite('det_results.jpg', plot_img)
print('图像已经保存为det_results.jpg了')