Abstract
The increasing portability of physical manuscripts to the digital environment makes it common for systems to offer automatic mechanisms for offline Handwritten Text Recognition (HTR). However, several scenarios and writing variations bring challenges in recognition accuracy, and, to minimize this problem, optical models can be used with language models to assist in decoding text. Thus, with the aim of improving results, dictionaries of characters and words are generated from the dataset and linguistic restrictions are created in the recognition process. In this way, this work proposes the use of spelling correction techniques for text post-processing to achieve better results and eliminate the linguistic dependence between the optical model and the decoding stage. In addition, an encoder–decoder neural network architecture in conjunction with a training methodology are developed and presented to achieve the goal of spelling correction. To demonstrate the effectiveness of this new approach, we conducted an experiment on five datasets of text lines, widely known in the field of HTR, three state-of-the-art Optical Models for text recognition and eight spelling correction techniques, among traditional statistics and current approaches of neural networks in the field of Natural Language Processing (NLP). Finally, our proposed spelling correction model is analyzed statistically through HTR system metrics, reaching an average sentence correction of 54% higher than the state-of-the-art method of decoding in the tested datasets.
Highlights
Writing is an essential communication and documentation tool worldwide
As we used several approaches for spelling correction, including some proposed neural network models, we performed the statistical analysis comparing the method with the best result against the traditional one, performed by Kaldi
In order to define our candidate for analysis, we considered the lowest average error rate in the metrics Character Error Rate (CER), Word Error Rate (WER), and Sequence Error Rate (SER)
Summary
Writing is an essential communication and documentation tool worldwide. Currently, in the digital age, the integration of physical manuscripts in the technological environment is becoming common, in which machines can understand the text of scanned images through the process of handwriting recognition and represent them in the digital context for later use [1]. Historical manuscripts [2], medical prescriptions [3], documents [4], and general forms [5] are some scenarios that require manual effort to digitize and transcribe content into the digital environment through Optical Character Recognition (OCR) technologies [6]. In this field, OCR systems have two categories: (i) online, in which the input information is obtained in real time through sensors; and (ii) offline, which obtains data from static scenarios, as in the case of images [7]. Unlike the printed text scenario, offline Handwritten Text Recognition (HTR) is more complex to achieve its goal, as for the same writer there are numerous variations in a single sentence [8]
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