Spotting Handwritten Words and REGEX using a two stage BLSTM-HMM architecture

Gautier Bideault (1, 2) , Luc Mioulet (1, 2) , Clément Chatelain (1, 2) , Thierry Paquet (1, 2)

1 LITIS - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes
2 DocApp - LITIS - Equipe Apprentissage

In this article, we propose a hybrid model for spotting words and regular expressions (REGEX) in handwritten documents. The model is made of the state-of-the-art BLSTM (Bidirectional Long Short Time Memory) neural network for recognizing and segmenting characters, coupled with a HMM to build line models able to spot the desired sequences. Experiments on the Rimes database show very promising results.

Mots clés: Regular Expression Spotting; REGEX; Handwritten Document; Handwriting Recognition; BLSTM; Word Spotting

Domaines: Traitement du texte et du document

Gautier Bideault, Luc Mioulet, Clément Chatelain, Thierry Paquet. Spotting Handwritten Words and REGEX using a two stage BLSTM-HMM architecture. Document Recognition and retrieval, 2015, San Francisco, United States. ⟨hal-01282956⟩