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Given the pseudo-trained model f θ, fine-tune the model on the gold-transcribed dataset D, with the loss function ℒ. Train the sequence-to-sequence model on the pseudo-annotated datasetS with the post-correction loss function ℒ from Section 3. It is used as additional information when computing c t and is added to the training loss to discourage the model from repeatedly attending to the same character (Mi et al., 2016 Tu et al., 2016).Īpply the initial OCR post-correction model f θ to each instance in the set U to obtain predictions using beam search inference.įor an instance x, let the prediction be f θ( x).Ĭreate a pseudo-annotated dataset with the predictions from step 1. Hence, the attention weights are expected to be higher closer to the diagonal-adding attention elements off the diagonal to the training loss encourages monotonic attention (Cohn et al., 2016).Ĭopy Mechanism: The copy mechanism enables the model to choose between generating a character based on the decoder state (Equation 1) or copying a character directly from the input sequence x by sampling from the attention distribution (Gu et al., 2016 See et al., 2017).Ĭoverage: The coverage vector keeps track of attention weights from previous timesteps. ( 2020) adapt the encoder-decoder model above for low-resource post-correction by adding pretraining and three structural biases:ĭiagonal Attention Loss: OCR post- correction is a monotonic sequence-to- sequence task. P y t = softmax W s t + b (1)Rijhwani et al. Results on four endangered languages demonstrate the utility of the proposed method, with relative error reductions of 15%–29%, where we find the combination of self-training and lexically aware decoding essential for achieving consistent improvements. In addition, to enforce consistency in the recognized vocabulary, we introduce a lexically aware decoding method that augments the neural post-correction model with a count-based language model constructed from the recognized texts, implemented using weighted finite-state automata (WFSA) for efficient and effective decoding. In this paper, we present a semi-supervised learning method that makes it possible to utilize these raw images to improve performance, specifically through the use of self-training, a technique where a model is iteratively trained on its own outputs. However, these methods rely on manually curated post- correction data, which are relatively scarce compared to the non-annotated raw images that need to be digitized. Optical character recognition (OCR) can be used to produce digitized text, and previous work has demonstrated the utility of neural post-correction methods that improve the results of general- purpose OCR systems on recognition of less- well-resourced languages.
SEMI OCR FONT HOW TO
Much of the existing linguistic data in many languages of the world is locked away in non- digitized books and documents. Usually for OCR I would just use thresholding and maybe blur, but since the font is semi-transparent and the background color changes a lot I’m not sure how to properly extract the data since the color of the edges of the font changes.