Question

I would like to know how practical it would be to create a program which takes handwritten characters in some form, analyzes them, and offers corrections to the user. The inspiration for this idea is to have elementary school students in other countries or University students in America learn how to write in languages such as Japanese or Chinese where there are a lot of characters and even the slightest mistake can make a big difference.

I am unsure how the program will analyze the character. My current idea is to get a single pixel width line to represent the stroke, compare how far each pixel is from the corresponding pixel in the example character loaded from a database, and output which area needs the most work. Endpoints will also be useful to know. I would also like to tell the user if their character could be interpreted as another character similar to the one they wanted to write.

I imagine I will need a library of some sort to complete this project in any sort of timely manner but I have been unable to locate one which meets the standards I will need for the program. I looked into OpenCV but it appears to be meant for vision than image processing. I would also appreciate the library/module to be in python or Java but I can learn a new language if absolutely necessary.

Thank you for any help in this project.

Answer 1

You can follow company links from this Wikipedia article:

http://en.wikipedia.org/wiki/Intelligent_character_recognition

I would not recommend that you attempt to implement a solution yourself, especially if you want to complete the task in less than a year or two of full-time work. It would be unfortunate if an incomplete solution provided poor guidance for students.

A word of caution: some companies that offer commercial ICR libraries may not wish to support you and/or may not provide a quote. That’s their right. However, if you do not feel comfortable working with a particular vendor, either ask for a different sales contact and/or try a different vendor first.

My current idea is to get a single pixel width line to represent the stroke, compare how far each pixel is from the corresponding pixel in the example character loaded from a database, and output which area needs the most work.

The initial step of getting a stroke representation only a single pixel wide is much more difficult than you might guess. Although there are simple algorithms (e.g. Stentiford and Zhang-Suen) to perform thinning, stroke crossings and rough edges present serious problems. This is a classic (and unsolved) problem. Thinning works much of the time, but when it fails, it can fail miserably.

You could work with an open source library, and although that will help you learn algorithms and their uses, to develop a good solution you will almost certainly need to dig into the algorithms themselves and understand how they work. That requires quite a bit of study.

Here are some books that are useful as introduct textbooks:

• Digital Image Processing by Gonzalez and Woods
• Character Recognition Systems by Cheriet, Kharma, Siu, and Suen
• Reading in the Brain by Stanislas Dehaene

Gonzalez and Woods is a standard textbook in image processing. Without some background knowledge of image processing it will be difficult for you to make progress.

The book by Cheriet, et al., touches on the state of the art in optical character recognition (OCR) and also covers handwriting recognition. The sooner you read this book, the sooner you can learn about techniques that have already been attempted.

The Dehaene book is a readable presentation of the mental processes involved in human reading, and could inspire development of interesting new algorithms.