Handwritten Word Spotting with Corrected Attributes

Abstract

We propose an approach to multi-writer word spotting, where the goal is to find a query word in a dataset comprised of document images. We propose an attributes-based approach that leads to a low-dimensional, fixed-length representation of the word images that is fast to compute and, especially, fast to compare. This approach naturally leads to an unified representation of word images and strings, which seamlessly allows one to indistinctly perform queryby-example, where the query is an image, and query-bystring, where the query is a string. We also propose a calibration scheme to correct the attributes scores based on Canonical Correlation Analysis that greatly improves the results on a challenging dataset. We test our approach on two public datasets showing state-of-the-art results.

Segmentation Based Word Spotting Method for Handwritten Documents

“…In order to show the effectiveness of our approach, we compared results of our approach with results of existing methods such as word profile features based approach [26], HOG based approach by extracting local gradient histogram features through sliding window [2] and Fisher vector representation computed over SIFT descriptors from the word image [14]. We have conducted experiments using database described above.…”

Tamil HandWritten Word Recognition with Entrenched Attributes using k-Means Algorithm

“…Yann Lecun et al [1] talked about different techniques connected to transcribed character acknowledgment and analyze them on a standard manually written digit acknowledgment assignment and depicted two frameworks for internet penman-ship acknowledgment that investigations show the upside of worldwide preparing, and the adaptability of chart transformer systems. Rejean Plamondon and Sargur N.Srihari [2] depicts the way of written by hand dialect, how it is transduced into electronic information and the fundamental ideas driving com-posed dialect acknowledgment calculations and they show that calculations for preprocessing, character and word acknowledgment, and execution with pragmatic frameworks.…”

“…Optical Character Recognition has picked up an energy since the requirement for digitizing or changing over checked pictures of machine printed or manually written content (numerals, letters, and images), in to an arrangement perceived by PCs, (for example, ASCII). OCR has been broadly utilized as the fundamental use of various learning techniques in machine learning writing [1]. Penmanship acknowledgment is the assignment of changing a dialect re-exhibited in its own particular spatial type of graphical imprints into a typical representation [2].…”

A Simple and Fast Word Spotting Method

“…There is substantial literature on word segmentation, including, for example, [13]. An example of word spotting among segmented images is [14]; among the works that do not require segmentation are [15] and [2].…”