更多详情参考[An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition](https://arxiv.org/pdf/1507.05717.pdf)
## 命令行预测
```shell
$ hub run chinese_ocr_db_rcnn --input_path"/PATH/TO/IMAGE"
"The module can recognize the chinese texts in an image. Firstly, it will detect the text box positions based on the differentiable_binarization_chn module. Then it recognizes the chinese texts. ",
images (list(numpy.ndarray)): images data, shape of each is [H, W, C]. If images not paths
paths (list[str]): The paths of images. If paths not images
use_gpu (bool): Whether to use gpu.
batch_size(int): the program deals once with one
output_dir (str): The directory to store output images.
visualization (bool): Whether to save image or not.
box_thresh(float): the threshold of the detected text box's confidence
text_thresh(float): the threshold of the recognize chinese texts' confidence
Returns:
res (list): The result of chinese texts and save path of images.
"""
ifuse_gpu:
try:
_places=os.environ["CUDA_VISIBLE_DEVICES"]
int(_places[0])
except:
raiseRuntimeError(
"Environment Variable CUDA_VISIBLE_DEVICES is not set correctly. If you wanna use gpu, please set CUDA_VISIBLE_DEVICES via export CUDA_VISIBLE_DEVICES=cuda_device_id."
# grab the left-most and right-most points from the sorted
# x-roodinate points
leftMost=xSorted[:2,:]
rightMost=xSorted[2:,:]
# now, sort the left-most coordinates according to their
# y-coordinates so we can grab the top-left and bottom-left
# points, respectively
leftMost=leftMost[np.argsort(leftMost[:,1]),:]
(tl,bl)=leftMost
rightMost=rightMost[np.argsort(rightMost[:,1]),:]
(tr,br)=rightMost
rect=np.array([tl,tr,br,bl],dtype="float32")
returnrect
@serving
defdetect_text(self,
images=[],
paths=[],
use_gpu=False,
output_dir='detection_result',
visualization=False,
box_thresh=0.5):
"""
Get the text box in the predicted images.
Args:
images (list(numpy.ndarray)): images data, shape of each is [H, W, C]. If images not paths
paths (list[str]): The paths of images. If paths not images
use_gpu (bool): Whether to use gpu. Default false.
output_dir (str): The directory to store output images.
visualization (bool): Whether to save image or not.
box_thresh(float): the threshold of the detected text box's confidence
Returns:
res (list): The result of text detection box and save path of images.
"""
ifuse_gpu:
try:
_places=os.environ["CUDA_VISIBLE_DEVICES"]
int(_places[0])
except:
raiseRuntimeError(
"Environment Variable CUDA_VISIBLE_DEVICES is not set correctly. If you wanna use gpu, please set CUDA_VISIBLE_DEVICES via export CUDA_VISIBLE_DEVICES=cuda_device_id."
