Image Based Character Recognition, Documentation System To Decode Inscription From Temple

• URL: http://arxiv.org/abs/2405.17449v1
• Date: Tue, 21 May 2024 17:20:35 GMT
• Title: Image Based Character Recognition, Documentation System To Decode Inscription From Temple
• Authors: Velmathi G, Shangavelan M, Harish D, Krithikshun M S,
• Abstract summary: This project undertakes the training and analysis of optical character recognition OCR methods applied to 10th century ancient Tamil inscriptions discovered on the walls of the Brihadeeswarar Temple. The chosen OCR methods include Tesseract,a widely used OCR engine,using modern ICR techniques to pre process the raw data and a box editing software to finetune our model. The analysis with Tesseract aims to evaluate their effectiveness in accurately deciphering the nuances of the ancient Tamil characters.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This project undertakes the training and analysis of optical character recognition OCR methods applied to 10th century ancient Tamil inscriptions discovered on the walls of the Brihadeeswarar Temple.The chosen OCR methods include Tesseract,a widely used OCR engine,using modern ICR techniques to pre process the raw data and a box editing software to finetune our model.The analysis with Tesseract aims to evaluate their effectiveness in accurately deciphering the nuances of the ancient Tamil characters.The performance of our model for the dataset are determined by their accuracy rate where the evaluated dataset divided into training set and testing set.By addressing the unique challenges posed by the script's historical context,this study seeks to contribute valuable insights to the broader field of OCR,facilitating improved preservation and interpretation of ancient inscriptions

Related papers

• DLoRA-TrOCR: Mixed Text Mode Optical Character Recognition Based On Transformer [12.966765239586994]
  Multi- fonts, mixed scenes and complex layouts seriously affect the recognition accuracy of traditional OCR models. We propose a parameter-efficient mixed text recognition method based on pre-trained OCR Transformer, namely DLoRA-TrOCR.
  arXiv  Detail & Related papers  (2024-04-19T09:28:16Z)
• LOCR: Location-Guided Transformer for Optical Character Recognition [55.195165959662795]
  We propose LOCR, a model that integrates location guiding into the transformer architecture during autoregression. We train the model on a dataset comprising over 77M text-location pairs from 125K academic document pages, including bounding boxes for words, tables and mathematical symbols. It outperforms all existing methods in our test set constructed from arXiv, as measured by edit distance, BLEU, METEOR and F-measure.
  arXiv  Detail & Related papers  (2024-03-04T15:34:12Z)
• Advancements and Challenges in Arabic Optical Character Recognition: A Comprehensive Survey [0.6629765271909505]
  This paper seeks to offer an exhaustive review of contemporary applications, methodologies, and challenges associated with Arabic Optical Character Recognition (OCR) A thorough analysis is conducted on prevailing techniques utilized throughout the OCR process, with a dedicated effort to discern the most efficacious approaches that demonstrate enhanced outcomes. In addition to presenting cutting-edge techniques and methods, this paper critically identifies research gaps within the realm of Arabic OCR.
  arXiv  Detail & Related papers  (2023-12-19T03:01:31Z)
• Data Generation for Post-OCR correction of Cyrillic handwriting [41.94295877935867]
  This paper focuses on the development and application of a synthetic handwriting generation engine based on B'ezier curves. Such an engine generates highly realistic handwritten text in any amounts, which we utilize to create a substantial dataset. We apply a Handwritten Text Recognition (HTR) model to this dataset to identify OCR errors, forming the basis for our POC model training.
  arXiv  Detail & Related papers  (2023-11-27T15:01:26Z)
• PHD: Pixel-Based Language Modeling of Historical Documents [55.75201940642297]
  We propose a novel method for generating synthetic scans to resemble real historical documents. We pre-train our model, PHD, on a combination of synthetic scans and real historical newspapers from the 1700-1900 period. We successfully apply our model to a historical QA task, highlighting its usefulness in this domain.
  arXiv  Detail & Related papers  (2023-10-22T08:45:48Z)
• On the Hidden Mystery of OCR in Large Multimodal Models [133.09809647230475]
  We conducted a comprehensive evaluation of Large Multimodal Models, such as GPT4V and Gemini, in various text-related visual tasks. Our study encompasses 29 datasets, making it the most comprehensive OCR evaluation benchmark available.
  arXiv  Detail & Related papers  (2023-05-13T11:28:37Z)
• Cleansing Jewel: A Neural Spelling Correction Model Built On Google OCR-ed Tibetan Manuscripts [12.346821696831805]
  We present a neural spelling correction model built on Google OCR-ed Tibetan Manuscripts to auto-correct OCR-ed noisy output. This paper is divided into four sections: dataset, model architecture, training and analysis.
  arXiv  Detail & Related papers  (2023-04-07T00:45:12Z)
• PreSTU: Pre-Training for Scene-Text Understanding [49.288302725486226]
  We propose PreSTU, a novel pre-training recipe dedicated to scene-text understanding (STU) PreSTU introduces OCR-aware pre-training objectives that encourage the model to recognize text from an image and connect it to the rest of the image content. We empirically demonstrate the effectiveness of this pre-training approach on eight visual question answering and four image captioning benchmarks.
  arXiv  Detail & Related papers  (2022-09-12T18:29:55Z)
• 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)
• TextOCR: Towards large-scale end-to-end reasoning for arbitrary-shaped scene text [23.04601165885908]
  We propose TextOCR, an arbitrary-shaped scene text detection and recognition with 900k annotated words collected on real images. We show that current state-of-the-art text-recognition (OCR) models fail to perform well on TextOCR. We use a TextOCR trained OCR model to create PixelM4C model which can do scene text based reasoning on an image in an end-to-end fashion.
  arXiv  Detail & Related papers  (2021-05-12T07:50:42Z)
• TEACHTEXT: CrossModal Generalized Distillation for Text-Video Retrieval [103.85002875155551]
  We propose a novel generalized distillation method, TeachText, for exploiting large-scale language pretraining. We extend our method to video side modalities and show that we can effectively reduce the number of used modalities at test time. Our approach advances the state of the art on several video retrieval benchmarks by a significant margin and adds no computational overhead at test time.
  arXiv  Detail & Related papers  (2021-04-16T17:55:28Z)

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.