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Improving the Accuracy of Tesseract 4.0 OCR Engine Using Convolution-Based Preprocessing
| Content Provider | MDPI |
|---|---|
| Author | Elena, Cușnir Sporici, Dan Boiangiu, Costin-Anton |
| Copyright Year | 2020 |
| Description | Optical Character Recognition (OCR) is the process of identifying and converting texts rendered in images using pixels to a more computer-friendly representation. The presented work aims to prove that the accuracy of the Tesseract 4.0 OCR engine can be further enhanced by employing convolution-based preprocessing using specific kernels. As Tesseract 4.0 has proven great performance when evaluated against a favorable input, its capability of properly detecting and identifying characters in more realistic, unfriendly images is questioned. The article proposes an adaptive image preprocessing step guided by a reinforcement learning model, which attempts to minimize the edit distance between the recognized text and the ground truth. It is shown that this approach can boost the character-level accuracy of Tesseract 4.0 from 0.134 to 0.616 (+359% relative change) and the F1 score from 0.163 to 0.729 (+347% relative change) on a dataset that is considered challenging by its authors. |
| Starting Page | 715 |
| e-ISSN | 20738994 |
| DOI | 10.3390/sym12050715 |
| Journal | Symmetry |
| Issue Number | 5 |
| Volume Number | 12 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-05-02 |
| Access Restriction | Open |
| Subject Keyword | Symmetry Remote Sensing Optical Character Recognition Convolution Tesseract Unsupervised Learning Reinforcement Learning Actor-critic Model Convolutional Neural Network |
| Content Type | Text |
| Resource Type | Article |
