Transfer Learning for a Letter-Ngrams to Word Decoder in the Context of Historical Handwriting Recognition with Scarce Resources
Lack of data can be an issue when beginning a new study on historical handwritten documents. In order to deal with this, we present the character-based decoder part of a multilingual approach based on transductive transfer learning for a historical handwriting recognition task on Italian Comedy Registers. The decoder must build a sequence of characters that corresponds to a word from a vector of letter-ngrams. As learning data, we created a new dataset from untapped resources that covers the same domain and period of our Italian Comedy data, as well as resources from common domains, periods, or languages. We obtain a 97.42{\%} Character Recognition Rate and a 86.57{\%} Word Recognition Rate on our Italian Comedy data, despite a lexical coverage of 67{\%} between the Italian Comedy data and the training data. These results show that an efficient system can be obtained by a carefully selecting the datasets used for the transfer learning.
Code (0)
등록된 구현이 없습니다.
Tasks
DecoderHandwriting RecognitionInformation RetrievalKeyword SpottingLanguage ModelingLanguage ModellingTransfer LearningSimilar Papers 제목 키워드 기반
Advancing Acoustic-to-Word CTC Model with Attention and Mixed-Units
The acoustic-to-word model based on the Connectionist Temporal Classification (CTC) criterion is a natural end-to-end (E2E) system directly targeting word as output unit. Two issues exist in the system: first, the curren…
DecoderLanguage ModelingLanguage ModellingAdvancing Acoustic-to-Word CTC Model
The acoustic-to-word model based on the connectionist temporal classification (CTC) criterion was shown as a natural end-to-end (E2E) model directly targeting words as output units. However, the word-based CTC model suff…
DecoderLanguage ModelingLanguage ModellingmodelMinimizing the Bag-of-Ngrams Difference for Non-Autoregressive Neural Machine Translation
Non-Autoregressive Neural Machine Translation (NAT) achieves significant decoding speedup through generating target words independently and simultaneously. However, in the context of non-autoregressive translation, the w…
Machine TranslationSentenceTranslationA Web Interface for Diachronic Semantic Search in Spanish
This article describes a semantic system which is based on distributional models obtained from a chronologically structured language resource, namely Google Books Syntactic Ngrams.The models were created using dependency…
Gated ConvNets for Letter-Based ASR
In this paper we introduce a new speech recognition system, leveraging a simple letter-based ConvNet acoustic model. The acoustic model requires only audio transcription for training -- no alignment annotations, nor any …
DecoderLanguage ModelingLanguage Modellingspeech-recognition+1