Word Spotting as a Service: An Unsupervised and Segmentation-Free Framework for Handwritten Documents

Abstract

Word spotting strategies employed in historical handwritten documents face many challenges due to variation in the writing style and intense degradation. In this paper, a new method that permits efficient and effective word spotting in handwritten documents is presented that relies upon document-oriented local features that take into account information around representative keypoints and a matching process that incorporates a spatial context in a local proximity search without using any training data. The method relies on a document-oriented keypoint and feature extraction, along with a fast feature matching method. This enables the corresponding methodological pipeline to be both effectively and efficiently employed in the cloud so that word spotting can be realised as a service in modern mobile devices. The effectiveness and efficiency of the proposed method in terms of its matching accuracy, along with its fast retrieval time, respectively, are shown after a consistent evaluation of several historical handwritten datasets.

Z-Transform-Based Profile Matching to Develop a Learning-Free Keyword Spotting Method for Handwritten Document Images

“…Due to these problems, researchers tried to search for better and more powerful features. Consequently, a good number of methods found in the literature that made use of directional features like the histogram of oriented gradients (HOG) [14,35], slit style HOG [36], local binary pattern (LBP) [14], projection of oriented gradients (POG) [37] and modified version of POG (mPOG) [4], pyramid histogram of oriented gradients (PHOG) [38], angular features [21], oriented basic image features (oBIFs) [15], and documentoriented local features (DoLFs) [39] to perform KWS using the learning-free approach. Rodríguez-Serrano and Perronnin [35] used two feature similarity measure techniques viz., hidden Markov model (HMM) and DTW but found HMM as superior.…”

“…They built several hypotheses in a bottom-up approach that is they first employed the text hypothesis and then used the line hypothesis to perform keyword spotting in the document images. In another work, Konstantinos et al [39] first extracted DoLFs from word images and then quantized them using BoVW methods. In other words, the authors used DoLFs instead of SIFT or HOG, generally used in BoVW models earlier.…”