Scan, Attend and Read: End-to-End Handwritten Paragraph Recognition with MDLSTM Attention
We present an attention-based model for end-to-end handwriting recognition. Our system does not require any segmentation of the input paragraph. The model is inspired by the differentiable attention models presented recently for speech recognition, image captioning or translation. The main difference is the covert and overt attention, implemented as a multi-dimensional LSTM network. Our principal contribution towards handwriting recognition lies in the automatic transcription without a prior segmentation into lines, which was crucial in previous approaches. To the best of our knowledge this is the first successful attempt of end-to-end multi-line handwriting recognition. We carried out experiments on the well-known IAM Database. The results are encouraging and bring hope to perform full paragraph transcription in the near future.
Code (0)
등록된 구현이 없습니다.
Tasks
Handwriting RecognitionImage CaptioningSegmentationspeech-recognitionSpeech RecognitionTranslationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
GatedLexiconNet: A Comprehensive End-to-End Handwritten Paragraph Text Recognition System
The Handwritten Text Recognition problem has been a challenge for researchers for the last few decades, especially in the domain of computer vision, a subdomain of pattern recognition. Variability of texts amongst writer…
Handwritten Text RecognitionSegmentationEnd-to-end Handwritten Paragraph Text Recognition Using a Vertical Attention Network
Unconstrained handwritten text recognition remains challenging for computer vision systems. Paragraph text recognition is traditionally achieved by two models: the first one for line segmentation and the second one for t…
DecoderHandwritten Text RecognitionTowards End-to-end Handwritten Document Recognition
Handwritten text recognition has been widely studied in the last decades for its numerous applications. Nowadays, the state-of-the-art approach consists in a three-step process. The document is segmented into text lines,…
Handwriting RecognitionHandwritten Document RecognitionHandwritten Text RecognitionFull Page Handwriting Recognition via Image to Sequence Extraction
We present a Neural Network based Handwritten Text Recognition (HTR) model architecture that can be trained to recognize full pages of handwritten or printed text without image segmentation. Being based on Image to Seque…
Handwriting RecognitionHandwritten Text RecognitionHTRImage Segmentation+2A Comprehensive Handwritten Paragraph Text Recognition System: LexiconNet
In this study, we have presented an efficient procedure using two state-of-the-art approaches from the literature of handwritten text recognition as Vertical Attention Network and Word Beam Search. The attention module i…
DecoderHandwritten Text Recognition