Handwritten Word Recognition using Deep Learning Approach: A Novel Way of Generating Handwritten Words

• URL: http://arxiv.org/abs/2303.07514v1
• Date: Mon, 13 Mar 2023 22:58:34 GMT
• Title: Handwritten Word Recognition using Deep Learning Approach: A Novel Way of Generating Handwritten Words
• Authors: Mst Shapna Akter, Hossain Shahriar, Alfredo Cuzzocrea, Nova Ahmed, Carson Leung
• Abstract summary: This paper proposes a novel way of generating diverse handwritten word images using handwritten characters. The whole approach shows the process of generating two types of large and diverse handwritten word datasets. For the experiments, we have targeted the Bangla language, which lacks the handwritten word dataset.
• Score: 14.47529728678643
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A handwritten word recognition system comes with issues such as lack of large and diverse datasets. It is necessary to resolve such issues since millions of official documents can be digitized by training deep learning models using a large and diverse dataset. Due to the lack of data availability, the trained model does not give the expected result. Thus, it has a high chance of showing poor results. This paper proposes a novel way of generating diverse handwritten word images using handwritten characters. The idea of our project is to train the BiLSTM-CTC architecture with generated synthetic handwritten words. The whole approach shows the process of generating two types of large and diverse handwritten word datasets: overlapped and non-overlapped. Since handwritten words also have issues like overlapping between two characters, we have tried to put it into our experimental part. We have also demonstrated the process of recognizing handwritten documents using the deep learning model. For the experiments, we have targeted the Bangla language, which lacks the handwritten word dataset, and can be followed for any language. Our approach is less complex and less costly than traditional GAN models. Finally, we have evaluated our model using Word Error Rate (WER), accuracy, f1-score, precision, and recall metrics. The model gives 39% WER score, 92% percent accuracy, and 92% percent f1 scores using non-overlapped data and 63% percent WER score, 83% percent accuracy, and 85% percent f1 scores using overlapped data.

Related papers

• 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)
• Is it an i or an l: Test-time Adaptation of Text Line Recognition Models [9.149602257966917]
  We introduce the problem of adapting text line recognition models during test time. We propose an iterative self-training approach that uses feedback from the language model to update the optical model. Experimental results show that the proposed adaptation method offers an absolute improvement of up to 8% in character error rate.
  arXiv  Detail & Related papers  (2023-08-29T05:44:00Z)
• Does color modalities affect handwriting recognition? An empirical study on Persian handwritings using convolutional neural networks [7.965705015476877]
  We investigate to see whether color modalities of handwritten digits and words affect their recognition accuracy or speed. We selected 13,330 isolated digits and 62,500 words from a novel Persian handwritten database. CNN on the BW digit and word images has a higher performance compared to the other two color modalities.
  arXiv  Detail & Related papers  (2023-07-22T19:47:52Z)
• Ensemble Transfer Learning for Multilingual Coreference Resolution [60.409789753164944]
  A problem that frequently occurs when working with a non-English language is the scarcity of annotated training data. We design a simple but effective ensemble-based framework that combines various transfer learning techniques. We also propose a low-cost TL method that bootstraps coreference resolution models by utilizing Wikipedia anchor texts.
  arXiv  Detail & Related papers  (2023-01-22T18:22:55Z)
• Kurdish Handwritten Character Recognition using Deep Learning Techniques [26.23274417985375]
  This paper attempts to design and develop a model that can recognize handwritten characters for Kurdish alphabets using deep learning techniques. A comprehensive dataset was created for handwritten Kurdish characters, which contains more than 40 thousand images. The tested results reported a 96% accuracy rate, and training accuracy reported a 97% accuracy rate.
  arXiv  Detail & Related papers  (2022-10-18T16:48:28Z)
• 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)
• Deduplicating Training Data Makes Language Models Better [50.22588162039083]
  Existing language modeling datasets contain many near-duplicate examples and long repetitives. Over 1% of the unprompted output of language models trained on these datasets is copied verbatim from the training data. We develop two tools that allow us to deduplicate training datasets.
  arXiv  Detail & Related papers  (2021-07-14T06:06:52Z)
• 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)
• Visually Grounded Compound PCFGs [65.04669567781634]
  Exploiting visual groundings for language understanding has recently been drawing much attention. We study visually grounded grammar induction and learn a constituency from both unlabeled text and its visual captions.
  arXiv  Detail & Related papers  (2020-09-25T19:07:00Z)
• Robust Handwriting Recognition with Limited and Noisy Data [7.617456558732551]
  We focus on learning handwritten characters from maintenance logs, a constrained setting where data is very limited and noisy. We break the problem into two consecutive stages of word segmentation and word recognition respectively and utilize data augmentation techniques to train both stages. Our system achieves a lower error rate and is more suited to handle noisy and difficult documents.
  arXiv  Detail & Related papers  (2020-08-18T20:33:23Z)
• Offline Handwritten Chinese Text Recognition with Convolutional Neural Networks [5.984124397831814]
  In this paper, we build the models using only the convolutional neural networks and use CTC as the loss function. We achieve 6.81% character error rate (CER) on the ICDAR 2013 competition set, which is the best published result without language model correction.
  arXiv  Detail & Related papers  (2020-06-28T14:34:38Z)

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.