Training Tesseract 5 for a New Font

Summary

This video tutorial guides viewers on training Tesseract OCR with a custom font for improved recognition. It covers generating ground truth data with images, text, and box files, using 'text2image' from Tesseract's training tools. The script provided automates creating single-line text files and images, setting up training with 'makefile', and evaluating model performance. Tips on adjusting parameters for better training outcomes are included.

Takeaways

• 😀 The video is a tutorial on training Tesseract, an optical character recognition engine, with a custom font to improve its recognition capabilities.
• 📄 To train Tesseract, you need to provide ground truth data which includes generating images with the custom font and corresponding text and box files that describe the content and location of the text.
• 🖼️ The script uses 'text2image' application from Tesseract's training tools to generate images from text files.
• 📝 The generated images should be in TIFF or PNG format, accompanied by a '.txt' file for the text and a '.box' file that describes the location of each character.
• 🔍 The video creator developed a Python script to automate the process of creating single-line text files from a large text file and generating the corresponding images and box files.
• 🔢 The script uses the 'unicharset' file from Tesseract to define the rules of English, which helps the neural network understand how words are formed.
• 💻 The video demonstrates how to set up the folder structure and run commands for training Tesseract with the new data.
• 🔄 The training process involves running iterations where Tesseract learns from the provided data, and the number of iterations can be adjusted based on the desired accuracy and time frame.
• 📊 The video shows how to evaluate the trained model using a test image and compares the results before and after training to demonstrate improvement.
• 🔧 The creator encourages viewers to modify the provided script to suit their needs, such as changing the font, model name, and output directory, to ensure a deeper understanding of the training process.

Q & A

What is the main purpose of training Tesseract with a custom font?

The main purpose is to improve Tesseract's recognition capabilities for a specific font that it may not recognize well by default.

What does 'ground truth' mean in the context of training Tesseract?

'Ground truth' refers to the correct or expected output that Tesseract should recognize from the images, which includes both the text and the location of each character.

What file formats are typically used for the image and ground truth data when training Tesseract?

For the image, TIFF or PNG formats are used, while a .txt file is used for the ground truth text and a .box file describes the location and character of each element in the image.

How can one generate images with a custom font for training Tesseract?

One can use the 'text2image' application that comes with Tesseract's training tools to generate images with the custom font.

What is a 'box file' and why is it necessary for training Tesseract?

A 'box file' describes the location and identity of each character in the image. It is necessary because it provides the ground truth data that Tesseract uses to learn where to find characters in the images.

What is the source of the training text used in the script?

The training text is sourced from the 'training-text' repository, specifically the English folder, which contains a large file full of English text.

How does the script separate the training text into single-line text files?

The script reads the large training text file and creates a separate text file for each line, then uses 'text2image' to generate an image and a box file for each line.

What is the significance of using the 'unicharset' file in training?

The 'unicharset' file contains the rules of English that help the neural network understand how words are formed, which is crucial for accurate recognition.

How can one evaluate the performance of a trained Tesseract model?

One can evaluate the model by running a command that uses the trained model to recognize text from an image and compares it against the expected output.

What is the recommended approach for increasing the accuracy of the trained model?

The recommended approach is to increase the number of training iterations and to use a larger dataset of images, while being cautious not to overfit the model.

How can one install and use a custom font for training Tesseract?

One can install the font on their system and then specify the font in the training script. On Linux, this might involve updating the font cache, while on Windows, it's as simple as installing the font and using it in the script.