We address the problem of segmenting and retrieving word images in collections of historical manuscripts given a text query. This is commonly referred to as "word spotting". To this end, we first p ...
@cite_51: 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.
A recent approach that can be seen as hybrid between sliding window and connected components is presented in @cite_51 . Here, the authors use a Resnet encoder-decoder network to produce a heatmap denoting each pixels probability of being inside, outside, or near a bounding box. The heatmap is smoothed using a smoother network before being fed to a Proposal Generation network that produces bounding box coordinates, which are subsequently filtered using a proposal Filter network.