Compact and Efficient WFST-Based Decoders for Handwriting Recognition

Abstract

We present two weighted finite-state transducer (WFST) based decoders for handwriting recognition. One decoder is a cloud-based solution that is both compact and efficient. The other is a device-based solution that has a small memory footprint. A compact WFST data structure is proposed for the cloud-based decoder. There are no output labels stored on transitions of the compact WFST. A decoder based on the compact WFST data structure produces the same result with significantly less footprint compared with a decoder based on the corresponding standard WFST. For the device-based decoder, on-the-fly language model rescoring is performed to reduce footprint. Careful engineering methods, such as WFST weight quantization, token and data type refinement, are also explored. When using a language model containing 600,000 n-grams, the cloud-based decoder achieves an average decoding time of 4.04 ms per text line with a peak footprint of 114.4 MB, while the device-based decoder achieves an average decoding time of 13.47 ms per text line with a peak footprint of 31.6 MB.