Toward a Dataset-Agnostic Word Segmentation Method

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.