无法改善文档图像的遮罩RCNN模型? [英] Unable to improve the mask RCNN model for document images?
问题描述
我正在训练一个模型，以便从我的简历中提取所有必要的字段，为此我使用遮罩rcnn来检测图像中的字段.我已经训练了我的遮罩RCNN模型以提取49个字段的1000个训练样本.我无法提高准确性.如何改进模型?是否有任何预训练的权重可能会有所帮助?
I am training a model to extract all the necessary fields from a resume for which I am using mask rcnn to detect the fields in image. I have trained my mask RCNN model for 1000 training samples with 49 fields to extract. I am unable to improve the accuracy. How to improve the model? Is there any pretrained weights that may help?
难以阅读以下文字-
推荐答案
就像您要进行文本分类/处理一样，您需要从文本中提取细节，但是您正在应用对象检测算法.我相信您需要使用OCR提取文本(如果您将cv作为图像)并使用文本分类模型.查看以下链接，了解有关文本分类的更多信息-
Looks like you want to do text classification/processing, you need to extract details from the text but you are applying object detection algorithms. I believe you need to use OCR to extract text (if you have cv as an image) and use the text classification model. Check out the below links more information about text classification -
https://www.tensorflow.org/tutorials/tensorflow_text/intro
这篇关于无法改善文档图像的遮罩RCNN模型?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
