One of our customers has a problem: they have millions of unorganized documents that have not been digitized yet. This makes finding the right document very complicated. Here at Avisi Labs, we are working on a digital solution for this issue. Our solution makes it possible to search for documents (once they are digital) by adding metadata, such as document type (e.g. "manual" or "technical specification"). In this blog, I will describe how we performed OCR (Optical Character Recognition) to extract the contents of documents using the Google Vision API and how we trained a machine learning model that is able to determine document types.
Extracting the contents from documents
Introduction to the Google Cloud Platform and the Vision API
The Google Cloud platform offers two computer vision products that use machine learning:
AutoML Vision gives you the opportunity to train your custom machine learning models by making use of a graphical interface.
Google Vision API offers pre-trained machine learning models that can be accessed through REST and RPC APIs. These models are able to perform actions like assigning labels to images, classifying images into millions of predefined categories and detecting objects and faces in images. They also contain a feature that is able to read printed and handwritten text from images and documents. This is the feature used in this project in our process to make our customers' documents searchable.
OCR (Optical Character Recognition)
We're using the Vision API feature to extract the contents from each document by performing OCR. The documents are sent to and from the API through the Go client library. This enables us to speed things up by using Goroutines. This is very useful because of the rate limits that Google sets to the use of the Vision API. To put this into numbers, we can only perform OCR on 16 images or a total of 8 MB per request.
We start things off by converting our documents from PDF files to JPEG images. Though OCR can be performed on PDF documents, our research found that the Vision API was unable to recognize text within images in PDF documents. The same problem occurs when a page is entirely made up of one image, which is something that is the case in our dataset of scanned documents. Want to know if your document can be put through Vision API? Open your document with a program like Adobe Acrobat Reader. If you are able to select individual words and sentences on the page, then those parts of the PDF document can be recognized by the Vision API. If not, the Vision API will not recognize this text. This problem can be avoided by sending the documents as JPEG images instead of PDF documents.
We convert our PDF documents to JPEG images using the Imagick library. Each page in a document is converted to a separate image. The converted images are sent to the Vision API and we get a JSON response for each image which includes the text that was detected in the image. The contents of each page are merged together to get one file that contains the contents of our document.
Example of OCR
Take for example the following PDF-file, the directions to our Avisi Tower:
The steps that we have to perform on this document are:
- Convert the PDF document to JPEG images (one image for each page in the document)
- Send the images to the Vision API using the Go client library
- Extract the contents from the responses and merge them together in one file.
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Training a model to predict document type
After we perform OCR and extract the actual contents from all of the PDF documents, we proceed by training a Natural Language Processing model. This model will be able to predict a document type based on the contents of the document.
When training a machine learning model, we can't just pass the raw text data as input. We have to create a feature vector. Features are numerical values which represent some characteristic of the contents of a document. The feature vector is created by calculating the Term Frequency, Inverse Document Frequency (TFIDF) for each term in our contents. This measurement represents the importance of the terms in each document.
- Term Frequency = The frequency of a term in a document. If a word is mentioned many times, it could be more important than terms that are only mentioned once.
- Inverse document frequency = The number of occurrences of a term compared to the number of occurrences in other documents. It is possible that some terms are mentioned across multiple documents. These terms could be less important because they are more common. For example, stop words or domain-specific language.
Our TFIDF Vectorizer considers both unigrams and bigrams. Unigrams are single terms and bigrams are pairs of terms. This distinction is made because a pair of terms may have a different meaning than a single term. For example, take the unigram market and compare it to the bigram stock market.
Calculating the statistics of the terms in our documents creates a TFIDF Vectorizer which contains a feature vector for each of our documents. In our project, the feature vector consists of 3596 different features per document. This feature vector enables us to train a machine learning model using supervised learning. But first, we must check if we can expect a good performing machine learning model.
Plotting our feature vector
We want to check if we can really classify different document types based on our feature vector. By plotting our feature vector for each document, we can see if there are some areas with clusters of one document type. The problem is that the feature vector consists of 3596 different features. This leads to a high-dimensional space. To be able to plot our vector, we use the dimensionality reduction technique t-SNE (t-distributed stochastic neighbor embedding). This keeps points that are nearby in the high-dimensional space close to each other in our projected space. As a result, we are able to plot our feature vectors in two dimensions, which can be seen in the figure below.
In this plot, the different colors correspond with the different document types. We clearly see some clusters of one document type, which indicates that it is possible to achieve a high accuracy classification when training a machine learning model with our feature vectors.
Training a model
Based on the feature vectors we've created and a known document type for our documents in the dataset, we can train the machine learning model. This model will be able to predict the correct document type for new documents. We used some supervised learning models from Scikit-learn, a Python package that provides these models. How well a model performs depends on the data you use. It is recommended to evaluate the accuracy of different models and choose the one that achieves the highest accuracy.
Once our model has been trained, it is possible to predict a document type for any new documents. By following the same steps, we perform OCR and create a feature vector for these documents. The feature vector is passed to the trained model and our model will predict the correct document type for new documents.
Key takeaways - how to classify documents using Artificial Intelligence
There are multiple steps you have to take to train a machine learning model that is able to predict the correct document type of a document:
- Collect your documents and scan them (if they are not digitized yet)
- Convert the documents to images
- Perform OCR on the images using the Google Vision API to extract the contents of the documents
- Set up feature vectors that represent some of the characteristics of the contents of the documents
- Check if you can expect a good performing machine learning model by plotting feature vectors in two dimensions
- Train supervised machine learning models and evaluate their accuracy
- Choose the best performing model and start predicting document types of new documents!