【文件属性】:
文件名称:Java OCR Framework
文件大小:13.61MB
文件格式:ZIP
更新时间:2020-04-28 03:38:38
OCR
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:
Normalization
Segmentation
Feature Extraction
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.
Fork it.
Create a branch (git checkout -b my_fancy_feature)
Commit your changes (git commit -am "Added amazing feature")
Push to the branch (git push origin my_fancy_feature)
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.
【文件预览】:
OCR-master
----neural_network.eg(73KB)
----build.xml(3KB)
----src()
--------jar()
--------mnist()
--------resources()
--------ocr()
--------common()
----nbproject()
--------build-impl.xml(76KB)
--------project.xml(496B)
--------genfiles.properties(467B)
--------project.properties(3KB)
----.gitignore(185B)
----README.md(3KB)
网友评论
- 学习学习,看看再说