Training an model for PDF files and comparing them to exisiting PDF's

General Discussion
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#1

Hi everybody!

I just made my first touches with TensorFlow and AI.
I have experience in programming so no worries abou that.
Currently i am working for a company which investigates and creates documents(PDF files) based on these investigations.

However the documents that are being created are consist mostly of the same lay-out. I would like to automate the process of checking those documents and learning an AI model (Tensorflow model if possible) to read all of the existing PDF files (Big data). To then compare them with a newly created document to scan if the document is good without any faults in it (i.e. signatures, spelling mistakes, dates, and the rest of the content).

Then it would either correct the new PDF file or make a small text file with faults in it.

I currently so far that i know that i need to convert the PDF files into images, make an OCR and fixing them.

How would i go about this in big lines? or is this too difficult for an AI model to do?

As of now i have 500+ pdf files as big data.

#3

Hi @Watzegik_Datzegik Welcome to Tensorflow Forum,

  1. Data Preparation:
  • PDF to Images: Convert your PDF files into images. You can use libraries like PyPDF2 or pdf2image for this task.
  • OCR: Apply Optical Character Recognition (OCR) to extract text from the images. Tesseract OCR is a popular open-source option, or you can use cloud-based OCR services like Google Cloud Vision API or Microsoft Azure Computer Vision.
  1. Data Annotation and Labeling:
  • Labeling Faults: Manually label your existing documents to identify faults such as signatures, spelling mistakes, dates, and other relevant content. This will serve as your training data.
  1. Model Training:
  • Choose Model: TensorFlow offers various pre-trained models suitable for text recognition tasks. You might want to start with models like Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs) for text recognition.
  • Data Augmentation: Augment your training data by introducing variations in fonts, sizes, and styles to make your model robust.
  • Training: Train your model on the annotated dataset to recognize and classify different types of faults in documents.
  1. Model Evaluation:
  • Validation: Evaluate the performance of your model using validation data to ensure it can correctly identify faults in documents.
  1. Document Analysis:
  • Apply Model: Once trained, use your model to analyze newly created documents. Convert them to images, perform OCR, and then feed the extracted text into your model for analysis.
  • Fault Detection: Let the model identify faults in the new documents such as missing signatures, spelling errors, or incorrect dates.
  1. Correction or Reporting:
  • Automatic Correction: Implement mechanisms to automatically correct faults in documents if possible. For example, use spell-checking libraries to fix spelling mistakes or add missing signatures programmatically.
  • Reporting: If automatic correction is not feasible, generate a report highlighting the detected faults and suggestions for correction.
  1. Iterative Improvement:
  • Continuous Training: Periodically retrain your model with new annotated data to improve its accuracy and adaptability to new document formats or types of faults.

Tools and Libraries:

  • TensorFlow: For building and training your AI model.
  • OpenCV: For image processing tasks like converting PDFs to images.
  • Tesseract OCR: For text extraction from images.
  • Natural Language Processing (NLP) Libraries: For tasks like spell checking and language processing.