The following topics are dealt with: learning (artificial intelligence); document image processing; feature extraction; text analysis; convolutional neural nets; image segmentation; handwritten character recognition; image classification; text detection; optical character recognition.

Not all languages and domains of handwriting have large labeled datasets available for training handwriting recognition (HWR) models. One way to address this problem is to leverage high resource languages to help train models for low resource languages. In this work, we adapt HWR models trained on a source language to a target language that uses the same writing script. We do so using only labeled data in the source language, unlabeled data in the target language, and a language model in the target language. The language model is used to produce target transcriptions to allow regular example based training. Using this approach we demonstrate improved transferability among French, English, and Spanish languages using both historical and modern handwriting datasets.

We introduce two data augmentation and normalization techniques, which, used with a CNN-LSTM, significantly reduce Word Error Rate (WER) and Character Error Rate (CER) beyond best-reported results on handwriting recognition tasks. (1) We apply a novel profile normalization technique to both word and line images. (2) We augment existing text images using random perturbations on a regular grid. We apply our normalization and augmentation to both training and test images. Our approach achieves low WER and CER over hundreds of authors, multiple languages and a variety of collections written centuries apart. Image augmentation in this manner achieves state-of-the-art recognition accuracy on several popular handwritten word benchmarks.