Body: <p>What is the best approach/model for classifying document images with over 70+ classes of documents? I have tried LayoutLM which is a model that incorporated both NLP and Computer vision to classify but it's accuracy is not upto the mark. Are there any other models/methods I could try to come up with a better solution?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-07-08T08:19:51.253Z
Comments.Text: It is difficult to answer your question like this:
  - you speak of image classification but you mention documents and tagged the question with *natural language processing*, so are you using images or text?
  - how much data do you have? Do you some computational power as well? The default answer would probably to use a deep learning model, but even that is quite vague. You could use a pre-trained model and fine-tune for example.
Comments.UserDisplayName: theophile
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-07-08T12:17:24.403Z
Comments.Text: What is a 'document image'?
Comments.UserDisplayName: Brian O'Donnell
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-07-10T03:56:34.600Z
Comments.Text: A document image is a scanned version of any document. The task is to classify these scanned images into their respective doc types. We use images and we need NLP because we need to extract the text out of these images and make sense out of it.
Comments.UserDisplayName: darthvader40
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-07-10T03:57:02.170Z
Comments.Text: The model LayoutLM has been used but it's accuracy is not upto the mark.
Comments.UserDisplayName: darthvader40
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-07-10T22:16:29.463Z
Comments.Text: @darthvader40 Edit your post to include all this info in the comments directly in the post, because comments are temporary. Also, please, share with us your findings so far, which shows that you've tried at least a little bit to answer your question first.
Comments.UserDisplayName: nbro
Tags: <machine-learning><natural-language-processing><algorithm-request>
Title: What is the best approach/model for classifying document images with over 70+ classes of documents?
UserDisplayName: darthvader40
ThreadText: # What is the best approach/model for classifying document images with over 70+ classes of documents?
  
  (Asked by: username_0 on 2022-07-08)
  
  What is the best approach/model for classifying document images with over 70+ classes of documents? I have tried LayoutLM which is a model that incorporated both NLP and Computer vision to classify but it's accuracy is not upto the mark. Are there any other models/methods I could try to come up with a better solution?
  
  **Tags:** machine-learning, natural-language-processing, algorithm-request
  > **Comments:**
  > **username_1**: It is difficult to answer your question like this: - you speak of image classification but you mention documents and tagged the question with *natural language processing*, so are you using images or text? - how much data do you have? Do you some computational power as well? The default answer would probably to use a deep learning model, but even that is quite vague. You could use a pre-trained model and fine-tune for example.  
  > **username_2**: What is a 'document image'?  
  > **username_0**: A document image is a scanned version of any document. The task is to classify these scanned images into their respective doc types. We use images and we need NLP because we need to extract the text out of these images and make sense out of it.  
  > **username_0**: The model LayoutLM has been used but it's accuracy is not upto the mark.  
  > **username_3**: @darthvader40 Edit your post to include all this info in the comments directly in the post, because comments are temporary. Also, please, share with us your findings so far, which shows that you've tried at least a little bit to answer your question first.  
  
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  ## 0 answers
  
  