Frequently Asked Questions

Sections

• General
• Using the Toolkit

General

Q: Who are target users for LipiTk ?

A: The toolkit has different components intended to support different types of users, with different requirements, such as application developers, technology enthusiasts, and researchers. See the Overview for details.

Q: What is in the toolkit ?

A: The toolkit is actually a collection of different kinds of components, all related to digital ink and handwriting recognition. Most (but not all) have source code along with binaries. The major categories of components are listed below.

• Core Toolkit - this is a single component containing algorithms and scripts for creating your own handwritten shape recognizers.
• Standalone tools for data collection etc.
• Applications and Demos built using the toolkit.
• “Pre-built” recognizers built using the Lipi Toolkit for many common character sets.

Q: What can you use LipiTk for ?

A: LipiTk gives you the capability to integrate handwriting recognition of isolated characters or shapes into your applications.  It does this using a variety of different components targeted at different types of users, e.g.

• Ready-to-use recognizers for common character sets (e.g.numerals) that you can integrate into your own applications.
• LipitkIDE, an interactive tool for creating a new recognizer for a custom set of shapes with just a few samples (this invisibly integrates the steps of data collection and training). You can then integrate these recognizers into your own applications.
• Data collection tools for collecting large numbers of samples of a set of characters/shapes, based on TabletPC and other devices.  You can then use the data to train one of the shape recognition methods provided in the Core Toolkit.
• Implementations of shape features and shape recognition methods in the Core Toolkit, which you can tweak or replace with your own algorithms.

Q: Can I use LipiTk components for my research or commercial application ? Do I have to return my source code changes ?

A: Check the license on that particular component. Most components in LipiTk (source code as well as binaries) are licensed under the MIT license, which places no restrictions on the type of use.  A few components which in turn use GPL'd parts may have a GPL license.

Q: How do I know what is in the pipeline ? How can I contribute my feedback and suggestions ?

A: We do have a published Roadmap that we try to keep up to date.  Please use the forums on SourceForge for your feedback and suggestions.

Q: Does LipiTk represent the state of the art in recognition technology ?

A: LipiTk uses well known and simple algorithms such as Nearest Neighbor classification using Dynamic Time Warping, and will give you good results as long it has it has been trained on sufficient data and configured correctly. However there are many advanced algorithms in the literature that may give better results. The purpose of Lipi Toolkit is to provide recognition technology primarily for character sets (and platforms) where commercial technology does not exist or is too expensive.

If you find the recognition accuracy from the available shape recognition methods unsatisfactory for your application, you are of course free to try your own algorithms with the toolkit, or use just the components you need.

Q: What kind of support can I expect ?

A: LipiTK is a community-oriented activity of HP Labs India, and as such, we do not have the means to support users. We do try and make detailed documentation and release notes available, and then there are the forums. You can also contact us via email, we will do what we can !

Q: How can I get involved ?

A: For starters, you can use different components of the toolkit and provide us with feedback.  We can use help with reviewing the documentation for errors, and creating user-friendly tutorials. If you are interested in creating recognizers for your language/script, we can provide some guidance with that.  You are also welcome to submit your own (sufficiently well-tested !) algorithms and demos. We would also like pointers to applications you have created using LipiTk components, and other resources (such as datasets) which may be useful to the community.  Visit the Community page for more ideas.

Using the Toolkit

Q: I am looking for a recognizer for common character sets such as numerals, English uppercase letters, etc.  What component should I use ?

A: Look through the available pre-built recognizers - perhaps there is one you can use. If not, and if you have access to a dataset of handwriting samples for that character set, you can train one of the shape recognition methods from the Core Toolkit using the dataset and create your own recognizer.

Q: I want to create a recognizer for a set of shapes that I have defined. How do I go about this without having to get too deep into the code ?

A: If you want to quickly build a recognizer for a custom set of shapes, try the LipitkIDE tool. This allows you to provide a few samples of each shape and builds a recognizer for you (using the DTW Shape Recognition Method internally). This is especially convenient when you are actively adding new shapes or modifying old ones, as in a gesture-based application. You will still need to integrate the recognizer into your application though - there is sample code available that illustrates how to do that.

If you have a fixed set of shapes (such as a character set for a script) and are looking for high accuracy, you may get better results by formally collecting data from a large number (say 100) users using one of the Data Collection Tools, and then training one of the Shape Recognition methods in the toolkit.

Q: I want to experiment with a different set of preprocessing methods/features/classification algorithms. How can I use the toolkit ?

A: If you want to try a different set of preprocessing algorithms or features, you can easily integrate it into the toolkit and train and test with the available recognition algorithms. If you want to try a different recognition technique or toolbox,  you can either implement that technique for Lipi Toolkit, or use Lipi Toolkit only to extract features to files, and do the recognition using your own implementation or toolbox.