An end-to-end, interactive Deep Learning based Annotation system for cursive and print English handwritten text

• URL: http://arxiv.org/abs/2304.08670v1
• Date: Tue, 18 Apr 2023 00:24:07 GMT
• Title: An end-to-end, interactive Deep Learning based Annotation system for cursive and print English handwritten text
• Authors: Pranav Guruprasad, Sujith Kumar S, Vigneswaran C, and V. Srinivasa Chakravarthy
• Abstract summary: We present an innovative, complete end-to-end pipeline, that annotates offline handwritten manuscripts written in both print and cursive English. This novel method involves an architectural combination of a detection system built upon a state-of-the-art text detection model, and a custom made Deep Learning model for the recognition system.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the surging inclination towards carrying out tasks on computational devices and digital mediums, any method that converts a task that was previously carried out manually, to a digitized version, is always welcome. Irrespective of the various documentation tasks that can be done online today, there are still many applications and domains where handwritten text is inevitable, which makes the digitization of handwritten documents a very essential task. Over the past decades, there has been extensive research on offline handwritten text recognition. In the recent past, most of these attempts have shifted to Machine learning and Deep learning based approaches. In order to design more complex and deeper networks, and ensure stellar performances, it is essential to have larger quantities of annotated data. Most of the databases present for offline handwritten text recognition today, have either been manually annotated or semi automatically annotated with a lot of manual involvement. These processes are very time consuming and prone to human errors. To tackle this problem, we present an innovative, complete end-to-end pipeline, that annotates offline handwritten manuscripts written in both print and cursive English, using Deep Learning and User Interaction techniques. This novel method, which involves an architectural combination of a detection system built upon a state-of-the-art text detection model, and a custom made Deep Learning model for the recognition system, is combined with an easy-to-use interactive interface, aiming to improve the accuracy of the detection, segmentation, serialization and recognition phases, in order to ensure high quality annotated data with minimal human interaction.

Related papers

• Efficiently Leveraging Linguistic Priors for Scene Text Spotting [63.22351047545888]
  This paper proposes a method that leverages linguistic knowledge from a large text corpus to replace the traditional one-hot encoding used in auto-regressive scene text spotting and recognition models. We generate text distributions that align well with scene text datasets, removing the need for in-domain fine-tuning. Experimental results show that our method not only improves recognition accuracy but also enables more accurate localization of words.
  arXiv  Detail & Related papers  (2024-02-27T01:57:09Z)
• CSSL-MHTR: Continual Self-Supervised Learning for Scalable Multi-script Handwritten Text Recognition [16.987008461171065]
  We explore the potential of continual self-supervised learning to alleviate the catastrophic forgetting problem in handwritten text recognition. Our method consists in adding intermediate layers called adapters for each task, and efficiently distilling knowledge from the previous model while learning the current task. We attain state-of-the-art performance on English, Italian and Russian scripts, whilst adding only a few parameters per task.
  arXiv  Detail & Related papers  (2023-03-16T14:27:45Z)
• Uncovering the Handwritten Text in the Margins: End-to-end Handwritten Text Detection and Recognition [0.840835093659811]
  This work presents an end-to-end framework for automatic detection and recognition of handwritten marginalia. It uses data augmentation and transfer learning to overcome training data scarcity. The effectiveness of the proposed framework has been empirically evaluated on the data from early book collections found in the Uppsala University Library in Sweden.
  arXiv  Detail & Related papers  (2023-03-10T14:00:53Z)
• DARE: A large-scale handwritten date recognition system [0.0]
  We introduce a database containing almost 10 million tokens, originating from more than 2.2 million handwritten dates. We show that training on handwritten text with high variability in writing styles result in robust models for general handwritten text recognition.
  arXiv  Detail & Related papers  (2022-10-02T12:47:36Z)
• Towards End-to-end Handwritten Document Recognition [0.0]
  Handwritten text recognition has been widely studied in the last decades for its numerous applications. In this thesis, we propose to tackle these issues by performing the handwritten text recognition of whole document in an end-to-end way. We reached state-of-the-art results at paragraph level on the RIMES 2011, IAM and READ 2016 datasets and outperformed the line-level state of the art on these datasets.
  arXiv  Detail & Related papers  (2022-09-30T10:31:22Z)
• Unified Pretraining Framework for Document Understanding [52.224359498792836]
  We present UDoc, a new unified pretraining framework for document understanding. UDoc is designed to support most document understanding tasks, extending the Transformer to take multimodal embeddings as input. An important feature of UDoc is that it learns a generic representation by making use of three self-supervised losses.
  arXiv  Detail & Related papers  (2022-04-22T21:47:04Z)
• Continuous Offline Handwriting Recognition using Deep Learning Models [0.0]
  Handwritten text recognition is an open problem of great interest in the area of automatic document image analysis. We have proposed a new recognition model based on integrating two types of deep learning architectures: convolutional neural networks (CNN) and sequence-to-sequence (seq2seq) The new proposed model provides competitive results with those obtained with other well-established methodologies.
  arXiv  Detail & Related papers  (2021-12-26T07:31:03Z)
• 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)
• Lexically Aware Semi-Supervised Learning for OCR Post-Correction [90.54336622024299]
  Much of the existing linguistic data in many languages of the world is locked away in non-digitized books and documents. Previous work has demonstrated the utility of neural post-correction methods on recognition of less-well-resourced languages. We present a semi-supervised learning method that makes it possible to utilize raw images to improve performance.
  arXiv  Detail & Related papers  (2021-11-04T04:39:02Z)
• SmartPatch: Improving Handwritten Word Imitation with Patch Discriminators [67.54204685189255]
  We propose SmartPatch, a new technique increasing the performance of current state-of-the-art methods. We combine the well-known patch loss with information gathered from the parallel trained handwritten text recognition system. This leads to a more enhanced local discriminator and results in more realistic and higher-quality generated handwritten words.
  arXiv  Detail & Related papers  (2021-05-21T18:34:21Z)
• Neural Language Modeling for Contextualized Temporal Graph Generation [49.21890450444187]
  This paper presents the first study on using large-scale pre-trained language models for automated generation of an event-level temporal graph for a document.
  arXiv  Detail & Related papers  (2020-10-20T07:08:00Z)

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.