Off-line Handwritten Text Recognition Research Articles

Gated Convolution and Stacked Self-Attention Encoder–Decoder-Based Model for Offline Handwritten Ethiopic Text Recognition

Offline handwritten text recognition (HTR) is a long-standing research project for a wide range of applications, including assisting visually impaired users, humans and robot interactions, and the automatic entry of business documents. However, due to variations in writing styles, visual similarities between different characters, overlap between characters, and source document noise, designing an accurate and flexible HTR system is challenging. The problem becomes serious when the algorithm has a low learning capacity and when the text used is complex and has a lot of characters in the writing system, such as Ethiopic script. In this paper, we propose a new model that recognizes offline handwritten Ethiopic text using a gated convolution and stacked self-attention encoder–decoder network. The proposed model has a feature extraction layer, an encoder layer, and a decoder layer. The feature extraction layer extracts high-dimensional invariant feature maps from the input handwritten image. Using the extracted feature maps, the encoder and decoder layers transcribe the corresponding text. For the training and testing of the proposed model, we prepare an offline handwritten Ethiopic text-line dataset (HETD) with 2800 samples and a handwritten Ethiopic word dataset (HEWD) with 10,540 samples obtained from 250 volunteers. The experiment results of the proposed model on HETD show a 9.17 and 13.11 Character Error Rate (CER) and Word Error Rate (WER), respectively. However, the model on HEWD shows an 8.22 and 9.17 CER and WER, respectively. These results and the prepared datasets will be used as a baseline for future research.

HTR for Greek Historical Handwritten Documents.

Offline handwritten text recognition (HTR) for historical documents aims for effective transcription by addressing challenges that originate from the low quality of manuscripts under study as well as from several particularities which are related to the historical period of writing. In this paper, the challenge in HTR is related to a focused goal of the transcription of Greek historical manuscripts that contain several particularities. To this end, in this paper, a convolutional recurrent neural network architecture is proposed that comprises octave convolution and recurrent units which use effective gated mechanisms. The proposed architecture has been evaluated on three newly created collections from Greek historical handwritten documents that will be made publicly available for research purposes as well as on standard datasets like IAM and RIMES. For evaluation we perform a concise study which shows that compared to state of the art architectures, the proposed one deals effectively with the challenging Greek historical manuscripts.

Sliding window based off-line handwritten text recognition using edit distance

Sliding window based off-line handwritten text recognition using edit distance

Offline Handwritten Text Recognition Using Deep Learning: A Review

The area of offline handwritten text recognition(OHTR) has been widely researched in the last decades, but it stills an important research problem. The OHTR system has an objective to transform a document image into text data. Compared with online handwriting recognition, the dynamic information about the writing trajectories in OHTR is not available. Many advancements have been proposed in the literature, most notably the application of deep learning methods to OHTR. In this paper, we introduced how this problem has been handled in the past few decades, analyze the latest advancements and the potential directions for future research in this field.

Boosting Offline Handwritten Text Recognition in Historical Documents With Few Labeled Lines

In this paper, we face the problem of offline handwritten text recognition (HTR) in historical documents when few labeled samples are available and some of them contain errors in the train set. Three main contributions are developed. First we analyze how to perform transfer learning (TL) from a massive database to a smaller historical database, analyzing which layers of the model need a fine-tuning process. Second, we analyze methods to efficiently combine TL and data augmentation (DA). Finally, an algorithm to mitigate the effects of incorrect labelings in the training set is proposed. The methods are analyzed over the ICFHR 2018 competition database, Washington and Parzival. Combining all these techniques, we demonstrate a remarkable reduction of CER (up to 6% in some cases) in the test set with little complexity overhead.

Towards the Natural Language Processing as Spelling Correction for Offline Handwritten Text Recognition Systems

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.

A Survey on Methods for Basic Unit Segmentation in Off-line Handwritten Text Recognition

A Survey on Methods for Basic Unit Segmentation in Off-line Handwritten Text Recognition

Unsupervised writer adaptation applied to handwritten text recognition

This paper deals with the problem of off-line handwritten text recognition. It presents a system of text recognition that exploits an original principle of adaptation to the handwriting to be recognized. The adaptation principle is based on the automatic learning, during the recognition, of the graphical characteristics of the handwriting. This on-line adaptation of the recognition system relies on the iteration of two steps: a word recognition step that allows to label the writer's representations (allographs) on the whole text and a re-evaluation step of character models. Tests carried out on a sample of 15 writers, all unknown by the system, show the interest of the proposed adaptation scheme since we obtain during iterations an improvement of recognition rates both at the letter and the word levels.