Siamese based Neural Network for Offline Writer Identification on word level data

• URL: http://arxiv.org/abs/2211.14443v1
• Date: Thu, 17 Nov 2022 10:01:46 GMT
• Title: Siamese based Neural Network for Offline Writer Identification on word level data
• Authors: Vineet Kumar and Suresh Sundaram
• Abstract summary: We propose a novel scheme to identify the author of a document based on the input word image. Our method is text independent and does not impose any constraint on the size of the input image under examination.
• Score: 7.747239584541488
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Handwriting recognition is one of the desirable attributes of document comprehension and analysis. It is concerned with the documents writing style and characteristics that distinguish the authors. The diversity of text images, notably in images with varying handwriting, makes the process of learning good features difficult in cases where little data is available. In this paper, we propose a novel scheme to identify the author of a document based on the input word image. Our method is text independent and does not impose any constraint on the size of the input image under examination. To begin with, we detect crucial components in handwriting and extract regions surrounding them using Scale Invariant Feature Transform (SIFT). These patches are designed to capture individual writing features (including allographs, characters, or combinations of characters) that are likely to be unique for an individual writer. These features are then passed through a deep Convolutional Neural Network (CNN) in which the weights are learned by applying the concept of Similarity learning using Siamese network. Siamese network enhances the discrimination power of CNN by mapping similarity between different pairs of input image. Features learned at different scales of the extracted SIFT key-points are encoded using Sparse PCA, each components of the Sparse PCA is assigned a saliency score signifying its level of significance in discriminating different writers effectively. Finally, the weighted Sparse PCA corresponding to each SIFT key-points is combined to arrive at a final classification score for each writer. The proposed algorithm was evaluated on two publicly available databases (namely IAM and CVL) and is able to achieve promising result, when compared with other deep learning based algorithm.

Related papers

• Attention based End to end network for Offline Writer Identification on Word level data [3.5829161769306244]
  We propose a writer identification system based on an attention-driven Convolutional Neural Network (CNN) The system is trained utilizing image segments, known as fragments, extracted from word images, employing a pyramid-based strategy. The efficacy of the proposed algorithm is evaluated on three benchmark databases.
  arXiv  Detail & Related papers  (2024-04-11T09:41:14Z)
• PART: Pre-trained Authorship Representation Transformer [64.78260098263489]
  Authors writing documents imprint identifying information within their texts: vocabulary, registry, punctuation, misspellings, or even emoji usage. Previous works use hand-crafted features or classification tasks to train their authorship models, leading to poor performance on out-of-domain authors. We propose a contrastively trained model fit to learn textbfauthorship embeddings instead of semantics.
  arXiv  Detail & Related papers  (2022-09-30T11:08:39Z)
• Offline Text-Independent Writer Identification based on word level data [7.747239584541488]
  This paper proposes a novel scheme to identify the authorship of a document based on handwritten input word images of an individual. We employ the SIFT algorithm to extract multiple key points at various levels of abstraction. These key points are then passed through a trained CNN network to generate feature maps corresponding to a convolution layer.
  arXiv  Detail & Related papers  (2022-02-21T13:32:09Z)
• Letter-level Online Writer Identification [86.13203975836556]
  We focus on a novel problem, letter-level online writer-id, which requires only a few trajectories of written letters as identification cues. A main challenge is that a person often writes a letter in different styles from time to time. We refer to this problem as the variance of online writing styles (Var-O-Styles)
  arXiv  Detail & Related papers  (2021-12-06T07:21:53Z)
• CRIS: CLIP-Driven Referring Image Segmentation [71.56466057776086]
  We propose an end-to-end CLIP-Driven Referring Image framework (CRIS) CRIS resorts to vision-language decoding and contrastive learning for achieving the text-to-pixel alignment. Our proposed framework significantly outperforms the state-of-the-art performance without any post-processing.
  arXiv  Detail & Related papers  (2021-11-30T07:29:08Z)
• Towards an IMU-based Pen Online Handwriting Recognizer [2.6707647984082357]
  We present a online handwriting recognition system for word recognition based on inertial measurement units (IMUs) This is obtained by means of a sensor-equipped pen that provides acceleration, angular velocity, and magnetic forces streamed via Bluetooth. Our model combines convolutional and bidirectional LSTM networks, and is trained with the Connectionist Temporal Classification loss.
  arXiv  Detail & Related papers  (2021-05-26T09:47:19Z)
• Arabic Handwritten Character Recognition based on Convolution Neural Networks and Support Vector Machine [0.0]
  We present an algorithm for recognizing Arabic letters and characters based on using deep convolution neural networks (DCNN) and support vector machine (SVM) This paper addresses the problem of recognizing the Arabic handwritten characters by determining the similarity between the input templates and the pre-stored templates. The experimental results of this work indicate the ability of the proposed algorithm to recognize, identify, and verify the input handwritten Arabic characters.
  arXiv  Detail & Related papers  (2020-09-28T16:18:52Z)
• Pairwise Learning for Name Disambiguation in Large-Scale Heterogeneous Academic Networks [81.00481125272098]
  We introduce Multi-view Attention-based Pairwise Recurrent Neural Network (MA-PairRNN) to solve the name disambiguation problem. MA-PairRNN combines heterogeneous graph embedding learning and pairwise similarity learning into a framework. Results on two real-world datasets demonstrate that our framework has a significant and consistent improvement of performance on the name disambiguation task.
  arXiv  Detail & Related papers  (2020-08-30T06:08:20Z)
• Neural Computing for Online Arabic Handwriting Character Recognition using Hard Stroke Features Mining [0.0]
  An enhanced method of detecting the desired critical points from vertical and horizontal direction-length of handwriting stroke features of online Arabic script recognition is proposed. A minimum feature set is extracted from these tokens for classification of characters using a multilayer perceptron with a back-propagation learning algorithm and modified sigmoid function-based activation function. The proposed method achieves an average accuracy of 98.6% comparable in state of art character recognition techniques.
  arXiv  Detail & Related papers  (2020-05-02T23:17:08Z)
• Expressing Objects just like Words: Recurrent Visual Embedding for Image-Text Matching [102.62343739435289]
  Existing image-text matching approaches infer the similarity of an image-text pair by capturing and aggregating the affinities between the text and each independent object of the image. We propose a Dual Path Recurrent Neural Network (DP-RNN) which processes images and sentences symmetrically by recurrent neural networks (RNN) Our model achieves the state-of-the-art performance on Flickr30K dataset and competitive performance on MS-COCO dataset.
  arXiv  Detail & Related papers  (2020-02-20T00:51:01Z)
• TextScanner: Reading Characters in Order for Robust Scene Text Recognition [60.04267660533966]
  TextScanner is an alternative approach for scene text recognition. It generates pixel-wise, multi-channel segmentation maps for character class, position and order. It also adopts RNN for context modeling and performs paralleled prediction for character position and class.
  arXiv  Detail & Related papers  (2019-12-28T07:52:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.