Fully Convolutional Networks for Handwriting Recognition

Abstract

Handwritten text recognition is challenging because of the virtually infinite ways a human can write the same message. Our fully convolutional handwriting model takes in a handwriting sample of unknown length and outputs an arbitrary stream of symbols. Our dual stream architecture uses both local and global context and mitigates the need for heavy preprocessing steps such as symbol alignment correction as well as complex post processing steps such as connectionist temporal classification, dictionary matching or language models. Using over 100 unique symbols, our model is agnostic to Latin-based languages, and is shown to be quite competitive with state of the art dictionary based methods on the popular IAM and RIMES datasets. When a dictionary is known, we further allow a probabilistic character error rate to correct errant word blocks. Finally, we introduce an attention based mechanism which can automatically target variants of handwriting, such as slant, stroke width, or noise.

Data-Driven Three-Phase Saturation Identification from X-ray CT Images with Critical Gas Hydrate Saturation

“…In most cases, the number of training data is several hundred or at most a few thousand for machine-learning applications in petroleum engineering [17][18][19][20][21][22][23][24][25][26][27][28], which was also noted in a previous paper [16]. Contrastingly, the number of training data is over ten thousand or even up to one million in computer-science-engineering-centered applications [29]. In spite of that, the number of training data does not necessarily guarantee reliability of training performance when inappropriate data is combined with the entire data pool.…”

Watch Your Strokes: Improving Handwritten Text Recognition with Deformable Convolutions

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“…The Weldegebriel [27] for classification proposes a hybrid model of two super classifiers: the CNN and the Extreme Gradient Boosting (XGBoost). CNN serves as an automatic training feature extractor for raw images for this integrated model, and XGBoost uses the extracted features as an input for recognition and classification.…”