Name
Abstract | ||
|---|---|---|
We present an efficient and effective approach to train OCR engines using the Aletheia document analysis system. All components required for training are seamlessly integrated into Aletheia: training data preparation, the OCR engine’s training processes themselves, text recognition, and quantitative evaluation of the trained engine. Such a comprehensive training and evaluation system, guided through a GUI, allows for iterative incremental training to achieve best results. The widely used Tesseract OCR engine is used as a case study to demonstrate the efficiency and effectiveness of the proposed approach. Experimental results are presented validating the training approach with two different historical datasets, representative of recent significant digitisation projects. The impact of different training strategies and training data requirements is presented in detail. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s10032-019-00347-8 | International Journal on Document Analysis and Recognition (IJDAR) |
Keywords | Field | DocType |
Optical character recognition, OCR, Machine learning, Training, Graphical user interface, Historical documents | Training set,Evaluation system,Document analysis,Computer science,Optical character recognition,Graphical user interface,Artificial intelligence,Tesseract,Machine learning,Text recognition | Journal |
Volume | Issue | ISSN |
23 | 1 | 1433-2833 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christian Clausner | 1 | 44 | 8.49 |
| A. Antonacopoulos | 2 | 404 | 35.43 |
| stefan pletschacher | 3 | 216 | 20.78 |