PLATTER: A Page-Level Handwritten Text Recognition System for Indic Scripts

• URL: http://arxiv.org/abs/2502.06172v1
• Date: Mon, 10 Feb 2025 05:50:26 GMT
• Title: PLATTER: A Page-Level Handwritten Text Recognition System for Indic Scripts
• Authors: Badri Vishal Kasuba, Dhruv Kudale, Venkatapathy Subramanian, Parag Chaudhuri, Ganesh Ramakrishnan,
• Abstract summary: We present an end-to-end framework for Page-Level hAndwriTTen TExt Recognition (PLATTER) Secondly, we demonstrate the usage of PLATTER to measure the performance of our language-agnostic HTD model. Finally, we release a Corpus of Handwritten Indic Scripts (CHIPS), a meticulously curated, page-level Indic handwritten OCR dataset.
• Score: 20.394597266150534
• License:
• Abstract: In recent years, the field of Handwritten Text Recognition (HTR) has seen the emergence of various new models, each claiming to perform competitively better than the other in specific scenarios. However, making a fair comparison of these models is challenging due to inconsistent choices and diversity in test sets. Furthermore, recent advancements in HTR often fail to account for the diverse languages, especially Indic languages, likely due to the scarcity of relevant labeled datasets. Moreover, much of the previous work has focused primarily on character-level or word-level recognition, overlooking the crucial stage of Handwritten Text Detection (HTD) necessary for building a page-level end-to-end handwritten OCR pipeline. Through our paper, we address these gaps by making three pivotal contributions. Firstly, we present an end-to-end framework for Page-Level hAndwriTTen TExt Recognition (PLATTER) by treating it as a two-stage problem involving word-level HTD followed by HTR. This approach enables us to identify, assess, and address challenges in each stage independently. Secondly, we demonstrate the usage of PLATTER to measure the performance of our language-agnostic HTD model and present a consistent comparison of six trained HTR models on ten diverse Indic languages thereby encouraging consistent comparisons. Finally, we also release a Corpus of Handwritten Indic Scripts (CHIPS), a meticulously curated, page-level Indic handwritten OCR dataset labeled for both detection and recognition purposes. Additionally, we release our code and trained models, to encourage further contributions in this direction.

Related papers

• Detecting Document-level Paraphrased Machine Generated Content: Mimicking Human Writing Style and Involving Discourse Features [57.34477506004105]
  Machine-generated content poses challenges such as academic plagiarism and the spread of misinformation. We introduce novel methodologies and datasets to overcome these challenges. We propose MhBART, an encoder-decoder model designed to emulate human writing style. We also propose DTransformer, a model that integrates discourse analysis through PDTB preprocessing to encode structural features.
  arXiv  Detail & Related papers  (2024-12-17T08:47:41Z)
• Retrieval is Accurate Generation [99.24267226311157]
  We introduce a novel method that selects context-aware phrases from a collection of supporting documents. Our model achieves the best performance and the lowest latency among several retrieval-augmented baselines.
  arXiv  Detail & Related papers  (2024-02-27T14:16:19Z)
• OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models [122.27878464009181]
  We conducted a comprehensive evaluation of Large Multimodal Models, such as GPT4V and Gemini, in various text-related visual tasks. OCRBench contains 29 datasets, making it the most comprehensive OCR evaluation benchmark available.
  arXiv  Detail & Related papers  (2023-05-13T11:28:37Z)
• 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)
• Boosting Modern and Historical Handwritten Text Recognition with Deformable Convolutions [52.250269529057014]
  Handwritten Text Recognition (HTR) in free-volution pages is a challenging image understanding task. We propose to adopt deformable convolutions, which can deform depending on the input at hand and better adapt to the geometric variations of the text.
  arXiv  Detail & Related papers  (2022-08-17T06:55:54Z)
• The LAM Dataset: A Novel Benchmark for Line-Level Handwritten Text Recognition [40.20527158935902]
  Handwritten Text Recognition (HTR) is an open problem at the intersection of Computer Vision and Natural Language Processing. We present the Ludovico Antonio Muratori dataset, a large line-level HTR dataset of Italian ancient manuscripts edited by a single author over 60 years.
  arXiv  Detail & Related papers  (2022-08-16T11:44:16Z)
• 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)
• Towards Document-Level Paraphrase Generation with Sentence Rewriting and Reordering [88.08581016329398]
  We propose CoRPG (Coherence Relationship guided Paraphrase Generation) for document-level paraphrase generation. We use graph GRU to encode the coherence relationship graph and get the coherence-aware representation for each sentence. Our model can generate document paraphrase with more diversity and semantic preservation.
  arXiv  Detail & Related papers  (2021-09-15T05:53:40Z)
• A Multiplexed Network for End-to-End, Multilingual OCR [20.818532124822713]
  We propose an E2E approach, Multiplexed Multilingual Mask TextSpotter, that performs script identification at the word level and handles different scripts with different recognition heads. Experiments show that our method outperforms the single-head model with similar number of parameters in end-to-end recognition tasks. We believe that our work is a step towards the end-to-end trainable and scalable multilingual multi-purpose OCR system.
  arXiv  Detail & Related papers  (2021-03-29T23:53:49Z)
• Learning to Select Bi-Aspect Information for Document-Scale Text Content Manipulation [50.01708049531156]
  We focus on a new practical task, document-scale text content manipulation, which is the opposite of text style transfer. In detail, the input is a set of structured records and a reference text for describing another recordset. The output is a summary that accurately describes the partial content in the source recordset with the same writing style of the reference.
  arXiv  Detail & Related papers  (2020-02-24T12:52:10Z)

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.