pprinz / ocr

An Optical Character Recognition Framework purely in Java

Java OCR Framework

An Optical Character Recognition Framework written purely in Java.

Installation

Build the project and add the jar for the project along with all the jars in the jar directory to your compile-time libraries.

Usage

There are 4 main parts to OCR:

1. Normalization
2. Segmentation
3. Feature Extraction
4. Classification

Feature Extraction and Classification are the only required parts. For Feature Extraction there are 5 algorithms at your disposal

• Horizontal Celled Projection
• Vertical Celled Projection
• Horizontal Projection Histogram
• Vertical Projection Histogram
• Local Line Fitting

This framework loosely uses a Fluent Interface Builder syntax.

Example:

OCR ocr = OCRBuilder
            .create()
            .normalization(new Normalization())
            .segmentation(new Segmentation())
            .featureExtraction(
                FeatureExtractionBuilder
                    .create()
                    .children(
                        new HorizontalCelledProjection(5),
                        new VerticalCelledProjection(5),
                        new HorizontalProjectionHistogram(),
                        new VerticalProjectionHistogram(),
                        new LocalLineFitting(49))
                    .build())
            .neuralNetwork(
                NeuralNetworkBuilder
                    .create()
                    .fromFile("neural_network.eg")
                    .build())
            .build();

Contributing

Want to help out? Feel free to share your ideas.

1. Fork it.
2. Create a branch (git checkout -b my_fancy_feature)
3. Commit your changes (git commit -am "Added amazing feature")
4. Push to the branch (git push origin my_fancy_feature)
5. Open a Pull Request

References

• Arora, Sandhya (2008). “Combining Multiple Feature Extraction Techniques for Handwritten Devnagari Character Recognition”, IEEE Region 10 Colloquium. pp. 342-348
• Haykin, Simon (1999). “Neural Networks A Comprehensive Foundation”, 2nd Edition. Pearson Education.
• Perez, Juan-Carlos ; Vidal, Enrique ; Sanchez, Lourdes (1994). “Simple and Effective Feature Extraction for Optical Character Recognition”, Selected Paper From the 5th Spanish Symposium on Pattern Recognition and Image Analysis.
• Zahid Hossain, M. ; Ashraful Amin, M. ; Yan, Hong (2012). “Rapid Feature Extraction for Optical Character Recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 6. pp. 801-813

Thanks

Thanks to Heaton Research for providing an amazing Neural Network framework. Also thanks to Apache Math Commons for doing all the math without the mess.