Image preprocessing and modified adaptive thresholding for improving OCR

• URL: http://arxiv.org/abs/2111.14075v2
• Date: Tue, 30 Nov 2021 04:04:33 GMT
• Title: Image preprocessing and modified adaptive thresholding for improving OCR
• Authors: Rohan Lal Kshetry
• Abstract summary: In this paper, I have proposed a method to find the major pixel intensity inside the text and thresholding an image accordingly. Based on the results obtained, it can be observed that this algorithm can be efficiently applied in the field of image processing for OCR.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper I have proposed a method to find the major pixel intensity inside the text and thresholding an image accordingly to make it easier to be used for optical character recognition (OCR) models. In our method, instead of editing whole image, I are removing all other features except the text boundaries and the color filling them. In this approach, the grayscale intensity of the letters from the input image are used as one of thresholding parameters. The performance of the developed model is finally validated with input images, with and without image processing followed by OCR by PyTesseract. Based on the results obtained, it can be observed that this algorithm can be efficiently applied in the field of image processing for OCR.

Related papers

• Decoder Pre-Training with only Text for Scene Text Recognition [54.93037783663204]
  Scene text recognition (STR) pre-training methods have achieved remarkable progress, primarily relying on synthetic datasets. We introduce a novel method named Decoder Pre-training with only text for STR (DPTR) DPTR treats text embeddings produced by the CLIP text encoder as pseudo visual embeddings and uses them to pre-train the decoder.
  arXiv  Detail & Related papers  (2024-08-11T06:36:42Z)
• Spherical Linear Interpolation and Text-Anchoring for Zero-shot Composed Image Retrieval [43.47770490199544]
  Composed Image Retrieval (CIR) is a complex task that retrieves images using a query, which is configured with an image and a caption. We introduce a novel ZS-CIR method that uses Spherical Linear Interpolation (Slerp) to directly merge image and text representations. We also introduce Text-Anchored-Tuning (TAT), a method that fine-tunes the image encoder while keeping the text encoder fixed.
  arXiv  Detail & Related papers  (2024-05-01T15:19:54Z)
• Optimization of Image Processing Algorithms for Character Recognition in Cultural Typewritten Documents [0.8158530638728501]
  This paper evaluates the impact of image processing methods and parameter tuning in Optical Character Recognition (OCR) The approach uses a multi-objective problem formulation to minimize Levenshtein edit distance and maximize the number of words correctly identified with a non-dominated sorting genetic algorithm (NSGA-II) Our findings suggest that employing image pre-processing algorithms in OCR might be more suitable for typologies where the text recognition task without pre-processing does not produce good results.
  arXiv  Detail & Related papers  (2023-11-27T11:44:46Z)
• Pixel-Inconsistency Modeling for Image Manipulation Localization [59.968362815126326]
  Digital image forensics plays a crucial role in image authentication and manipulation localization. This paper presents a generalized and robust manipulation localization model through the analysis of pixel inconsistency artifacts. Experiments show that our method successfully extracts inherent pixel-inconsistency forgery fingerprints.
  arXiv  Detail & Related papers  (2023-09-30T02:54:51Z)
• Beyond Learned Metadata-based Raw Image Reconstruction [86.1667769209103]
  Raw images have distinct advantages over sRGB images, e.g., linearity and fine-grained quantization levels. They are not widely adopted by general users due to their substantial storage requirements. We propose a novel framework that learns a compact representation in the latent space, serving as metadata.
  arXiv  Detail & Related papers  (2023-06-21T06:59:07Z)
• Extending TrOCR for Text Localization-Free OCR of Full-Page Scanned Receipt Images [0.07673339435080445]
  We propose a localization-free document-level OCR model for transcribing all the characters in a receipt image into an ordered sequence end-to-end. Specifically, we finetune the pretrained instance-level model TrOCR with randomly cropped image chunks. In our experiments, the model finetuned with our strategy achieved 64.4 F1-score and a 22.8% character error rate.
  arXiv  Detail & Related papers  (2022-12-11T15:45:26Z)
• Self-supervised Character-to-Character Distillation for Text Recognition [54.12490492265583]
  We propose a novel self-supervised Character-to-Character Distillation method, CCD, which enables versatile augmentations to facilitate text representation learning. CCD achieves state-of-the-art results, with average performance gains of 1.38% in text recognition, 1.7% in text segmentation, 0.24 dB (PSNR) and 0.0321 (SSIM) in text super-resolution.
  arXiv  Detail & Related papers  (2022-11-01T05:48:18Z)
• Levenshtein OCR [20.48454415635795]
  A novel scene text recognizer based on Vision-Language Transformer (VLT) is presented. Inspired by Levenshtein Transformer in the area of NLP, the proposed method explores an alternative way for automatically transcribing textual content from cropped natural images.
  arXiv  Detail & Related papers  (2022-09-08T06:46:50Z)
• 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)
• Unknown-box Approximation to Improve Optical Character Recognition Performance [7.805544279853116]
  A novel approach is presented for creating a customized preprocessor for a given OCR engine. Experiments with two datasets and two OCR engines show that the presented preprocessor is able to improve the accuracy of the OCR up to 46% from the baseline.
  arXiv  Detail & Related papers  (2021-05-17T16:09:15Z)
• The Power of Triply Complementary Priors for Image Compressive Sensing [89.14144796591685]
  We propose a joint low-rank deep (LRD) image model, which contains a pair of complementaryly trip priors. We then propose a novel hybrid plug-and-play framework based on the LRD model for image CS. To make the optimization tractable, a simple yet effective algorithm is proposed to solve the proposed H-based image CS problem.
  arXiv  Detail & Related papers  (2020-05-16T08:17:44Z)

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.