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

Abstract

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.

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

“…But such a trial cannot handle the scaling effect on handwriting words and, the skew and slant corrections are error-prone [34]. Moreover, when a word image is formed off many connected components then it is critical to estimate the profiles [9,21].…”

“…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.…”

“…However, the most important pre-requite of a learning-based method is a voluminous annotated training data [25][26][27], and preparing the training data is a cumbersome task. Moreover, their generalization is poor compared to learning-free approaches [4,21,28 i.e., a model trained on one dataset may perform poorly (generating poor results compared to the learningfree approach) on another dataset if not trained again [25,28]. These are the reasons that guide us to develop a learning-free KWS system.…”

“…Third, depending upon the underlying matching protocol used between query word and target word, we can classify a KWS technique as learning-free [4,20,21] and learning-based [22,23]. As the name suggests, in a learning-based method, the model is first trained on a large image database and then searches the target word set.…”

Semi-ProtoPNet Deep Neural Network for the Classification of Defective Power Grid Distribution Structures

“…The acronym VGG refers to Visual Geometry Group; it is a standard multilayer deep CNN architecture. Deep refers to the number of layers, e.g., VGG-13 [55], VGG-16 [56], and VGG-19 [57], have 13, 16, and 19 convolutional layers, respectively. These models are structured as a series of convolutional layers, which can efficiently extract features from the data.…”