Hough Transform-Based Angular Features for Learning-Free Handwritten Keyword Spotting

Sensors

Yoon Young Moon¹  Zong Woo Geem¹  Ram Sarkar²  Samir Malakar³  Subhranil Kundu⁴  Pawan Kumar Singh⁵ 

[1] College of IT Convergence, Gachon University, 1342 Seongnam Daero, Seongnam 13120, Korea;Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India;Department of Computer Science, Asutosh College, Kolkata 700026, India;Department of Electronics and Communication Engineering, National Institute of Technology Durgapur, Durgapur 713209, India;Department of Information Technology, Jadavpur University, Kolkata 700106, India;

关键词: dynamic time warping;    handwritten word;    Hough transform;    keyword spotting;    query by example;   

DOI  :  10.3390/s21144648

来源: DOAJ

【 摘 要 】

Handwritten keyword spotting (KWS) is of great interest to the document image research community. In this work, we propose a learning-free keyword spotting method following query by example (QBE) setting for handwritten documents. It consists of four key processes: pre-processing, vertical zone division, feature extraction, and feature matching. The pre-processing step deals with the noise found in the word images, and the skewness of the handwritings caused by the varied writing styles of the individuals. Next, the vertical zone division splits the word image into several zones. The number of vertical zones is guided by the number of letters in the query word image. To obtain this information (i.e., number of letters in a query word image) during experimentation, we use the text encoding of the query word image. The user provides the information to the system. The feature extraction process involves the use of the Hough transform. The last step is feature matching, which first compares the features extracted from the word images and then generates a similarity score. The performance of this algorithm has been tested on three publicly available datasets: IAM, QUWI, and ICDAR KWS 2015. It is noticed that the proposed method outperforms state-of-the-art learning-free KWS methods considered here for comparison while evaluated on the present datasets. We also evaluate the performance of the present KWS model using state-of-the-art deep features and it is found that the features used in the present work perform better than the deep features extracted using InceptionV3, VGG19, and DenseNet121 models.

【 授权许可】

Unknown