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Fine-Tuning an OCR Model
Optical character recognition (OCR) is probably one of the oldest applications of machine learning.
A simple experiment: fine-tuning an LSTM (long-short term memory) neural net to identify font in-game for the MMORPG OldSchool RuneScape. (Back in 2021, I trained an object recognition model for the game).
Before generative models, OpenAI focused on playing video games – starting with the OpenAI Gym and later Dota 2 bots. Why did I choose RuneScape? It's a fairly simple but deep game. It requires lots of clicking and optimization but doesn't require deep strategy. Most of all, the client is (mostly) open-sourced.
Generate training data. We're going to use synthetic training data. Tesseract has a utility that will generate images a corresponding character bounding boxes from text (text2image). While I could have used a variety of text corpora, I chose to use a bunch of text found in the game. I used the database in osrsbox-db. I found the fonts in RuneStar/fonts. I could have performed some data augmentation but chose not to – for this use case; the text is always printed uniformly (same spacing, orientation, etc.).
Training. There are generally three choices:
Train a model from scratch – Since we have a large amount of training data, we can train a model from scratch. Tesseract's training scripts don't
Fine-tune a model – Using the english Tesseract LSTM, fine-tune it to understand a new font or new characters.
Train the top layers – Randomize and retrain the top layers of the network. Useful if you're teaching it a new language with completely new charactesrs (e.g., an elfish font).
I tried both fine-tuning and training a new model from scratch. I did this a few times, as I found that (1) training was quick on my machine 64GB RAM and (2) it was easy to overfit the model.
For inference, I took raw screenshots from the game and processed them minimally. This is probably where most optimization could come from – preparing the data so that it's optimal for the model: not too bold, not too thin, black text on white background, non-text artifacts removed.
A few observations about the process:
Tesseract is a legacy OCR engine that's retroactively added neural network support. However, this means that training and inference are still done through the binary, not something more modern (like TensorFlow or PyTorch).
Preparing the training data and preparing the data for inference take up the most time and are essential to good results. Much of this is done manually – thresholding, color segmentation, and other image manipulation techniques to separate text. More modern architectures probably just train different models for each of these steps.
Tesseract finds it hard to identify pixelated fonts.
You can find the code (and the trained models) on my GitHub.