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Handwritten character (English letters, kanji,etc.) analysis and correction

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By User



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.


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Comments

  1. Tips For AllFebruary 21, 2012 at 2:18 PM

    Character Recognition is usually implemented using Artificial Neural Networks (ANNs). It is not a straightforward task to implement seeing that there are usually lots of ways in which different people write the same character.

    The good thing about neural networks is that they can be trained. So, to change from one language to another all you need to change are the weights between the neurons, and leave your network intact. Neural networks are also able to generalize to a certain extent, so they are usually able to cope with minor variances of the same letter.

    Tesseract is an open source OCR which was developed in the mid 90's. You might want to read about it to gain some pointers.

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  2. Tips For AllFebruary 21, 2012 at 2:18 PM

    Have you seen http://www.skritter.com? They do this in combination with spaced recognition scheduling.

    I guess you want to classify features such as curves in your strokes (http://en.wikipedia.org/wiki/CJK_strokes), then as a next layer identify componenents, then estimate the most likely character. All the while statistically weighting the most likely character. Where there are two likely matches you will want to show them as likely to be confused. You will also need to create a database of probably 3000 to 5000 characters, or up to 10000 for the ambitious.

    See also http://www.tegaki.org/ for an open source program to do this.

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