Toward a Dataset-Agnostic Word Segmentation Method

Gregory Axler, Lior Wolf

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Word segmentation in documents is a critical stage towards word and character recognition, as well as word spotting. Despite recent advancements in word segmentation and object detection, detecting instances of words in a cluttered handwritten document remains a non-trivial task that requires a large amount of labeled documents for training. We present a flexible and general framework for word segmentation in handwritten documents, which incorporates techniques from the recent object detection literature as well as document analysis tools. Our method utilizes information that is relevant for word segmentation and ignores other highly variable information contained in a handwritten text, thus allowing for efficient transfer learning between datasets and alleviating the need for labeled training data. Our approach efficiently detects words in a variety of scanned document images, including historical handwritten documents and modern day handwritten documents, presenting excellent results on existing benchmarks. In addition, we demonstrate the usefulness of our approach by achieving state-of-the-art results for segmentation-free word spotting tasks.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781479970612
StatePublished - 29 Aug 2018
Event25th IEEE International Conference on Image Processing, ICIP 2018 - Athens, Greece
Duration: 7 Oct 201810 Oct 2018

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880


Conference25th IEEE International Conference on Image Processing, ICIP 2018


  • Document Analysis
  • Object Detection
  • Transfer Learning


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