Text Line Extraction in Historical Documents Using Mask R-CNN

Ahmad Droby, Berat Kurar Barakat, Reem Alaasam, Boraq Madi, Irina Rabaev, Jihad El-Sana

Research output: Contribution to journalArticlepeer-review

Abstract

Text line extraction is an essential preprocessing step in many handwritten document image analysis tasks. It includes detecting text lines in a document image and segmenting the regions of each detected line. Deep learning-based methods are frequently used for text line detection. However, only a limited number of methods tackle the problems of detection and segmentation together. This paper proposes a holistic method that applies Mask R-CNN for text line extraction. A Mask R-CNN model is trained to extract text lines fractions from document patches, which are further merged to form the text lines of an entire page. The presented method was evaluated on the two well-known datasets of historical documents, DIVA-HisDB and ICDAR 2015-HTR, and achieved state-of-the-art results. In addition, we introduce a new challenging dataset of Arabic historical manuscripts, VML-AHTE, where numerous diacritics are present. We show that the presented Mask R-CNN-based method can successfully segment text lines, even in such a challenging scenario.

Original languageAmerican English
Pages (from-to)535-549
Number of pages15
JournalSignals
Volume3
Issue number3
DOIs
StatePublished - 1 Sep 2022

Keywords

  • deep learning
  • handwritten documents image analysis
  • historical documents
  • Mask R-CNN
  • text line extraction

All Science Journal Classification (ASJC) codes

  • Engineering (miscellaneous)

Fingerprint

Dive into the research topics of 'Text Line Extraction in Historical Documents Using Mask R-CNN'. Together they form a unique fingerprint.

Cite this