Document Type
Article
Publication Date
12-2009
Publication Title
Pattern Recognition
Abstract
This paper develops word recognition methods for historical handwritten cursive and printed documents. It employs a powerful segmentation-free letter detection method based upon joint boosting with histograms of gradients as features. Efficient inference on an ensemble of hidden Markov models can select the most probable sequence of candidate character detections to recognize complete words in ambiguous handwritten text, drawing on character n" role="presentation" style="box-sizing: border-box; margin: 0px; padding: 0px; display: inline-block; line-height: normal; font-size: 16.2px; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; position: relative;">n-gram and physical separation models. Experiments with two corpora of handwritten historic documents show that this approach recognizes known words more accurately than previous efforts, and can also recognize out-of-vocabulary words.
Keywords
Character recognition, Cursive text, Historical text
Volume
42
Issue
12
First Page
3338
Last Page
3347
DOI
doi.org/10.1016/j.patcog.2009.01.012
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Rights
© the authors
Recommended Citation
Howe, Nicholas; Feng, Shaolei; and Manmatha, R., "Finding Words in Alphabet Soup: Inference on Freeform Character Recognition for Historical Scripts" (2009). Computer Science: Faculty Publications, Smith College, Northampton, MA.
https://scholarworks.smith.edu/csc_facpubs/124
Comments
Author’s submitted manuscript.