Ocr font variations
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There is a mechanism defined in the new standard to bridge the gapīetween the old world and the new, called the StyleĪttributes (STAT) table. Relationships between individual font files and font "families."
#OCR FONT VARIATIONS SOFTWARE#
Such new functionality will, at least in some cases, mean thatĪpplication software will have to be reworked in order to present theĪvailable font variations to the end user in a meaningful fashion.īut there is another change inherent in the new feature that may notīe as obvious at first glance. Permutation of their features (weight, width, slant, etc). Instance at runtime based on interpolating the masters in a particular This enables the renderer to generate a new font Which makes it possible to encode multiple design "masters" into a Repeat step 1 to step 4 until a specified stopping criterion (e.g., no improvement in the validation set performance or reaching the maximum permitted iterations).In last week's look at the new revision of the OpenType fontįormat, we focused primarily on the new variations font feature, 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.
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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. Much of the existing linguistic data in many languages of the world is locked away in non- digitized books and documents.