Connectionist Temporal Classification Research Articles

Integrated End-to-End automatic speech recognition for languages for agglutinative languages

The relevance of the problem of automatic speech recognition lies in the lack of research for low-resource languages, stemming from limited training data and the necessity for new technologies to enhance efficiency and performance. The purpose of this work was to study the main aspects of integrated end-to-end speech recognition and the use of modern technologies in the natural processing of agglutinative languages, including Kazakh. In this article, the study of language models was carried out using comparative, graphic, statistical and analytical-synthetic methods, which were used in combination. This paper addresses automatic speech recognition (ASR) in agglutinative languages, particularly Kazakh, through a unified neural network model that integrates both acoustic and language modeling. Employing advanced techniques like connectionist temporal classification and attention mechanisms, the study focuses on effective speech-to-text transcription for languages with complex morphologies. Transfer learning from high-resource languages helps mitigate data scarcity in languages such as Kazakh, Kyrgyz, Uzbek, Turkish, and Azerbaijani. The research assesses model performance, underscores ASR challenges, and proposes advancements for these languages. It includes a comparative analysis of phonetic and word-formation features in agglutinative Turkic languages, using statistical data. The findings aid further research in linguistics and technology for enhancing speech recognition and synthesis, contributing to voice identification and automation processes.

Factorized and progressive knowledge distillation for CTC-based ASR models

Knowledge distillation (KD) is a popular model compression method to improve the performance of lightweight models by transferring knowledge from a teacher model to a student model. However, applying KD to connectionist temporal classification (CTC) ASR model is challenging due to its peaky posterior property. In this paper, we propose to address this issue by treating non-blank and blank frames differently for two main reasons. First, the non-blank frames in the teacher model’s posterior matrix and hidden representations provide more acoustic and linguistic information than the blank frames, but the frame number of non-blank frames only accounts for a small fraction of all frames, leading to a severe learning imbalance problem. Second, the non-blank tokens in the teacher’s blank-frame posteriors exhibit irregular probability distributions, negatively impacting the student model’s learning. Thus, we propose to factorize the distillation of non-blank and blank frames and further combine them into a progressive KD framework, which contains three incremental stages to facilitate the student model gradually building up its knowledge. The first stage involves a simple binary classification KD task, in which the student learns to distinguish between non-blank and blank frames, as the two types of frames are learned separately in subsequent stages. The second stage is a factorized representation-based KD, in which hidden representations are divided into non-blank and blank frames so that both can be distilled in a balanced manner. In the third stage, the student learns from the teacher’s posterior matrix through our proposed method, factorized KL-divergence (FKL), which performs different operation on blank and non-blank frame posteriors to alleviate the imbalance issue and reduce the influence of irregular probability distributions. Compared to the baseline, our proposed method achieves 22.5% relative CER reduction on the Aishell-1 dataset, 23.0% relative WER reduction on the Tedlium-2 dataset, and 17.6% relative WER reduction on the LibriSpeech dataset. To show the generalization of our method, we also evaluate our method on the hybrid CTC/Attention architecture as well as on scenarios with cross-model topology KD.

SilentInterpreter: Analysis of Lip Movement and Extracting Speech Using Deep Learning

Lip reading presents a captivating avenue for advancing speech recognition algorithms, leveraging visual cues from lip movements to recognise spoken words. This paper introduces a novel method employing deep neural networks to convert lip motions into textual representations. The methodology integrates convolutional neural networks for visual feature extraction, recurrent neural networks to capture temporal context, and the Connectionist Temporal Classification loss function for aligning lip features with phonemes. Additionally, dynamic learning rate scheduling and a unique callback mechanism for training visualization are incorporated into the process. Post-training on a sizeable dataset, the model demonstrates notable convergence, showcasing its ability to discern intricate temporal correlations. Comprehensive evaluations, combining quantitative metrics and qualitative assessments, validate the model's effectiveness. Visual inspections of lip reading capabilities and standard speech recognition criteria evaluation highlight its performance. The study delves into the impact of various model topologies and hyperparameters on performance, providing valuable insights for future research directions. This research contributes a deep learning framework for accurate and efficient speech recognition, expanding the landscape of lip reading technologies. The findings open paths for further refinement and deployment across diverse domains, including assistive technologies, audio-visual communication systems, and human-computer interaction. Keywords: lip reading, deep learning, convolutional neural networks, recurrent neural networks, CTC loss, speech recognition

Survey on Silentinterpreter :  Analysis of Lip Movement and  Extracting Speech using Deep Learning

Lip reading is a complex but interesting path for the growth of speech recognition algorithms. It is the ability of deciphering spoken words by evaluating visual cues from lip movements. In this study, we suggest a unique method for lip reading that converts lip motions into textual representations by using deep neural networks. Convolutional neural networks are used in the methodology to extract visual features, recurrent neural networks are used to simulate temporal context, and the Connectionist Temporal Classification loss function is used to align lip features with corresponding phonemes. The study starts with a thorough investigation of data loading methods, which include alignment extraction and video preparation. A well selected dataset with video clips and matching phonetic alignments is presented. We select relevant face regions, convert frames to grayscale, then standardize the resulting data so that it can be fed into a neural network. The neural network architecture is presented in depth, displaying a series of bidirectional LSTM layers for temporal context understanding after 3D convolutional layers for spatial feature extraction. Careful consideration of input shapes, layer combinations, and parameter selections forms the foundation of the model's design. To train the model, we align predicted phoneme sequences with ground truth alignments using the CTC loss. Dynamic learning rate scheduling and a unique callback mechanism for training visualization of predictions are integrated into the training process. After training on a sizable dataset, the model exhibits remarkable convergence and proves its capacity to understand intricate temporal correlations. Through the use of both quantitative and qualitative evaluations, the results are thoroughly assessed. We visually check the model's lip reading abilities and assess its performance using common speech recognition criteria. It is explored how different model topologies and hyperparameters affect performance, offering guidance for future research. The trained model is tested on external video samples to show off its practical application. Its accuracy and resilience in lip-reading spoken phrases are demonstrated. By providing a deep learning framework for precise and effective speech recognition, this research adds to the rapidly changing field of lip reading devices. The results offer opportunities for additional development and implementation in various fields, such as assistive technologies, audio-visual communication systems, and human-computer interaction.

Enhancing Arabic Handwritten Recognition System-Based CNN-BLSTM Using Generative Adversarial Networks

Arabic Handwritten Recognition (AHR) presents unique challenges due to the complexity of Arabic script and the limited availability of training data. This paper proposes an approach that integrates generative adversarial networks (GANs) for data augmentation within a robust CNN-BLSTM architecture, aiming to significantly improve AHR performance. We employ a CNN-BLSTM network coupled with connectionist temporal classification (CTC) for accurate sequence modeling and recognition. To address data limitations, we incorporate a GANs based data augmentation module trained on the IFN-ENIT Arabic handwriting dataset to generate realistic and diverse synthetic samples, effectively augmenting the original training corpus. Extensive evaluations on the IFN-ENIT benchmark demonstrate the efficacy of adopted approach. We achieve a recognition rate of 95.23%, surpassing the baseline model by 3.54%. This research presents a promising approach to data augmentation in AHR and demonstrates a significant improvement in word recognition accuracy, paving the way for more robust and accurate AHR systems.

Self-Distillation Regularized Connectionist Temporal Classification Loss for Text Recognition: A Simple Yet Effective Approach

Text recognition methods are gaining rapid development. Some advanced techniques, e.g., powerful modules, language models, and un- and semi-supervised learning schemes, consecutively push the performance on public benchmarks forward. However, the problem of how to better optimize a text recognition model from the perspective of loss functions is largely overlooked. CTC-based methods, widely used in practice due to their good balance between performance and inference speed, still grapple with accuracy degradation. This is because CTC loss emphasizes the optimization of the entire sequence target while neglecting to learn individual characters. We propose a self-distillation scheme for CTC-based model to address this issue. It incorporates a framewise regularization term in CTC loss to emphasize individual supervision, and leverages the maximizing-a-posteriori of latent alignment to solve the inconsistency problem that arises in distillation between CTC-based models. We refer to the regularized CTC loss as Distillation Connectionist Temporal Classification (DCTC) loss. DCTC loss is module-free, requiring no extra parameters, longer inference lag, or additional training data or phases. Extensive experiments on public benchmarks demonstrate that DCTC can boost text recognition model accuracy by up to 2.6%, without any of these drawbacks.

A closer look at reinforcement learning-based automatic speech recognition

Reinforcement learning (RL) has demonstrated effectiveness in improving model performance and robustness for automatic speech recognition (ASR) tasks. Researchers have employed RL-based training strategies to enhance performance beyond conventional supervised or semi-supervised learning. However, existing approaches treat RL as a supplementary tool, leaving the untapped potential of RL training largely unexplored. In this paper, we formulate a novel pure RL setting where an ASR model is trained exclusively through RL via human feedback metrics, e.g., word error rate (WER) or binary reward. This approach promises to significantly simplify the annotation process if we could replace the conventional onerous annotation with a single numeric value in a human–computer interaction (HCI) way. Our experiments demonstrate the feasibility of this new setting and also identify two main inherent issues in conventional RL-based ASR training that may lead to performance degradation: (1) the mismatch issue between the action and reward has been commonly overlooked in Connectionist Temporal Classification (CTC) based models, which is attributed to the inherent CTC alignment mapping issue; (2) the classic exploration–exploitation trade-off still exists in the sampling stage of RL-based ASR, and finding the balance between them becomes a challenge. To address these issues, we first propose a new RL-based approach named CTC-aligned Policy Gradient (CTC-PG), which provides a unified formulation for different sampling strategies and alleviates the mismatch issue of action and reward in CTC-based models. Moreover, we propose Focal Sampling to balance the trade-off between exploration and exploitation with a flexible temperature parameter. Experiment results on LibriSpeech dataset showcase the effectiveness and robustness of our methods by harnessing the full potential of RL in training ASR models.

The Evaluation of a Code-Switched Sepedi-English Automatic Speech Recognition System

Speech technology is a field that encompasses various techniques and tools used to enable machines to interact with speech, such as automatic speech recognition (ASR), spoken dialog systems, and others, allowing a device to capture spoken words through a microphone from a human speaker. End-to-end approaches such as Connectionist Temporal Classification (CTC) and attention-based methods are the most used for the development of ASR systems. However, these techniques were commonly used for research and development for many high-resourced languages with large amounts of speech data for training and evaluation, leaving low-resource languages relatively underdeveloped. While the CTC method has been successfully used for other languages, its effectiveness for the Sepedi language remains uncertain. In this study, we present the evaluation of the SepediEnglish code-switched automatic speech recognition system. This end-to-end system was developed using the Sepedi Prompted Code Switching corpus and the CTC approach. The performance of the system was evaluated using both the NCHLT Sepedi test corpus and the Sepedi Prompted Code Switching corpus. The model produced the lowest WER of 41.9%, however, the model faced challenges in recognizing the Sepedi only text

Enhancement of handwritten text recognition using AI-based hybrid approach

Handwritten text recognition (HTR) within computer vision and image processing stands as a prominent and challenging research domain, holding significant implications for diverse applications. Among these, it finds usefulness in reading bank checks, prescriptions, and deciphering characters on various forms. Optical character recognition (OCR) technology, specifically tailored for handwritten documents, plays a pivotal role in translating characters from a range of file formats, encompassing both word and image documents. Challenges in HTR encompass intricate layout designs, varied handwriting styles, limited datasets, and less accuracy achieved.Recent advancements in Deep Learning and Machine Learning algorithms, coupled with the vast repositories of unprocessed data, have propelled researchers to achieve remarkable progress in HTR. This paper aims to address the challenges in handwritten text recognition by proposing a hybrid approach. The primary objective is to enhance the accuracy of recognizing handwritten text from images. Through the integration of Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) with a Connectionist Temporal Classification (CTC) decoder, the results indicate substantial improvement. The proposed hybrid model achieved an impressive 98.50% and 98.80% accuracy on the IAM and RIMES datasets, respectively. This underscores the potential and efficacy of the consecutive use of these advanced neural network architectures in enhancing handwritten text recognition accuracy.•The proposed method introduces a hybrid approach for handwritten text recognition, employing CNN and BiLSTM with CTC decoder.•Results showcase a remarkable accuracy improvement of 98.50% and 98.80% on IAM and RIMES datasets, emphasizing the potential of this model for enhanced accuracy in recognizing handwritten text from images.

Improving Continuous Sign Language Recognition with Consistency Constraints and Signer Removal

Deep-learning-based continuous sign language recognition (CSLR) models typically consist of a visual module, a sequential module, and an alignment module. However, the effectiveness of training such CSLR backbones is hindered by limited training samples, rendering the use of a single connectionist temporal classification loss insufficient. To address this limitation, we propose three auxiliary tasks to enhance CSLR backbones. First, we enhance the visual module, which is particularly sensitive to the challenges posed by limited training samples, from the perspective of consistency. Specifically, since sign languages primarily rely on signers’ facial expressions and hand movements to convey information, we develop a keypoint-guided spatial attention module that directs the visual module to focus on informative regions, thereby ensuring spatial attention consistency. Furthermore, recognizing that the output features of both the visual and sequential modules represent the same sentence, we leverage this prior knowledge to better exploit the power of the backbone. We impose a sentence embedding consistency constraint between the visual and sequential modules, enhancing the representation power of both features. The resulting CSLR model, referred to as consistency-enhanced CSLR, demonstrates superior performance on signer-dependent datasets, where all signers appear during both training and testing. To enhance its robustness for the signer-independent setting, we propose a signer removal module based on feature disentanglement, effectively eliminating signer-specific information from the backbone. To validate the effectiveness of the proposed auxiliary tasks, we conduct extensive ablation studies. Notably, utilizing a transformer-based backbone, our model achieves state-of-the-art or competitive performance on five benchmarks, including PHOENIX-2014, PHOENIX-2014-T, PHOENIX-2014-SI, CSL, and CSL-Daily. Code and models are available at https://github.com/2000ZRL/LCSA_C2SLR_SRM.

Segmentation-free Recognition Algorithm Based on Deep Learning for Handwritten Text Image

Segmentation-based offline handwritten character recognition algorithms suffered from the segmenting difficulty of interleaving and touching in handwritten manuscripts. To tackle the problem, a segmentation-free recognition algorithm based on deep learning network is proposed in this paper. The network consists of four neural layers, including input layer for image preprocessing, CNNs layer for feature extraction, BDLSTM layer for sequence prediction, and connectionist temporal classification layer for text sequence alignment and classification. Besides, a novel data processing method is performed for data length equalization. Based on this, groups of experiments, based on six typical databases, involved in evaluation indicators of character correct rate, training time cost, storage space cost and testing time cost are carried out.  The experimental results show that the proposed algorithm has better performances in accuracy and efficiency than other classical algorithms.

An approach for handwritten Chinese text recognition unifying character segmentation and recognition

Text line recognition methods can be categorized into explicit segmentation based and implicit segmentation based ones. Explicit segmentation based methods require character-level annotation during training, while implicit segmentation based methods, trained on line-level annotated data, face alignment drift challenges. Though some methods have been proposed to address these challenges using weakly supervised object detection, they often rely on cumbersome pseudo-box generation processes and complex decoding. In this paper, we propose a unified framework to overcome these challenges, achieving high accuracy in text recognition and character segmentation. To eliminate the need of character-level annotated real text line data in training, we introduce a novel training paradigm that utilizes character-level annotated synthetic data and line-level annotated real data jointly. For synthetic data, candidate characters are explicitly aligned with labeled characters to generate hard labels for supervising model training. For real data, implicit alignment is produced by Connectionist Temporal Classification (CTC) mapping to provide soft labels for weakly-supervised model training. And for inference, we propose two decoding strategies leveraging the advantages of Non-Maximum Suppression (NMS) and CTC decoding. Extensive experiments on benchmark datasets demonstrate the superior performance of our method in text recognition and character localization, even with minimal amounts of character-level annotated line data.

LPSRGAN: Generative adversarial networks for super-resolution of license plate image

This paper proposes a super-resolution algorithm for reconstructing license plate images based on generative adversarial networks (GAN) for improving the recognition rate of low-resolution license plate images. To this end, this paper first designs a new image degradation model, called the n-stage random combination degradation model (n-RCD), which extends the traditional degradation model horizontally and vertically to better simulate the features of low-resolution license plate images in natural scenes. Subsequently, to address the problem that existing GAN models cannot adapt to the complex degradation space in natural scenes, this paper proposes a new network structure called LPSRGAN, which optimizes the SRGAN model from the generator and discriminator to improve its super-resolution capability. In addition, this paper also proposes a perceptual optical character recognition (OCR) loss for license plate images, which defines the connectionist temporal classification (CTC) loss of the output of the OCR network with character label values to better preserve the character details and features of the license plate images. Finally, the experiments reported in this paper show that the proposed algorithm improves the recognition rate of the original low-resolution license plate image by 12.48%, and the recognition rate of the reconstructed image reaches 93.90%. Based on this, this paper also tests the algorithm on actual captured data, and the results show that it performs well in natural scenes, demonstrating the algorithm has broad application prospects.

MECOS: A bilingual Manipuri–English spontaneous code-switching speech corpus for automatic speech recognition

In this study, we introduce a new code-switched speech database with 57h of Manipuri–English annotated spontaneous speech. Manipuri is an official language of India and is primarily spoken in the north–eastern Indian state of Manipur. Most Manipur native speakers today are bilingual and frequently use code switching in everyday discussions. By carefully assessing the amount of code-switched speech in each video, recordings from YouTube are gathered. 21,339 utterances and 291,731 instances of code switching are present in the database. Given the code-switching nature of the data, a proper annotation procedure is used, and the data are manually annotated using the Meitei Mayek unicode font and the roman alphabets for Manipuri and English, respectively. The transcription includes the information of the speakers, non-speech information, and the corresponding annotation. The aim of this research is to construct an automatic speech recognition (ASR) system as well as offer a thorough analysis and details of the speech corpus. We believe that our research is the first to use an ASR system for Manipuri–English code-switched speech. To evaluate the performance, ASR systems based on hybrid deep neural network and hidden Markov model (DNN–HMM), time delay neural network (TDNN), hybrid time delay neural network and long short-term memory (TDNN–LSTM) and three end-to-end (E2E) models i.e. hybrid connectionist temporal classification and attention model (CTC-Attention), Conformer, wav2vec XLSR are developed for Manipuri–English language. In comparison to other models, pure TDNN produces outcomes that are clearly superior.

Convolutional Arabic handwriting recognition system based BLSTM-CTC using WBS decoder

Arabic handwriting recognition (AHR) poses major challenges for pattern recognition due to the cursive script and visual similarity of Arabic characters. While deep learning demonstrates promise, architectural enhancements may further improve performance. This study presents an offline AHR approach using a convolutional neural network (CNN) with bidirectional long short-term memory (BLSTM) and connectionist temporal classification (CTC). By enhancing temporal modeling and context representations without segmentation requirements, this BLSTM-CTC-CNN framework with an integrated Word Beam Search (WBS) decoder achieved 94.58% accuracy on the IFN/ENIT database. Results highlight improved efficiency over prior works. This demonstrates continued advancement in sophisticated deep learning techniques for accurate AHR through specialized modeling of Arabic script cursive properties and decoding constraints. This research represents an advancement in the continuous development of progressively intricate and precise systems for handwriting recognition.

American Sign Language Recognition and Translation Using Perception Neuron Wearable Inertial Motion Capture System.

Sign language is designed as a natural communication method to convey messages among the deaf community. In the study of sign language recognition through wearable sensors, the data sources are limited, and the data acquisition process is complex. This research aims to collect an American sign language dataset with a wearable inertial motion capture system and realize the recognition and end-to-end translation of sign language sentences with deep learning models. In this work, a dataset consisting of 300 commonly used sentences is gathered from 3 volunteers. In the design of the recognition network, the model mainly consists of three layers: convolutional neural network, bi-directional long short-term memory, and connectionist temporal classification. The model achieves accuracy rates of 99.07% in word-level evaluation and 97.34% in sentence-level evaluation. In the design of the translation network, the encoder-decoder structured model is mainly based on long short-term memory with global attention. The word error rate of end-to-end translation is 16.63%. The proposed method has the potential to recognize more sign language sentences with reliable inertial data from the device.

A textual question answering and handwritten answer evaluation system for hindi language

Textual Question Answering targets answering questions defined in natural language. Question Answering Systems offer an automated approach to procuring answers to queries expressed in natural language. The need for Multilingual Question Answering without performing machine translation is ever existing. Besides that, automating tasks with the help of technology to assist humans, has been the main aim of research in recent years. This paper presents an automated answer evaluation system for reading comprehension-based questions in the Hindi language without requiring translation in any other language. The system accepts text, question, and handwritten answer of a student in the form of an image for answer evaluation. This is accomplished by developing a textual question-answering system for reading comprehension. It is an extractive approach that utilizes RoBERTa transformer model and fine-tunes it for Hindi question-answering. The answer to the question is extracted as a span from the provided text. Further, a handwritten text recognizer model is developed employing a Convolutional Recurrent Neural Network with Connectionist Temporal Classification module along with two layers of Bidirectional LSTM. Experimentation is performed using existing as well as self-created datasets to show the effectiveness of the proposed approach. An accuracy of 98.69% is obtained on the self-created Hindi-QA dataset and the proposed system outperformed the other existing methods. The paper also discusses potential research directions in the field.

GLaLT: Global-Local Attention-Augmented Light Transformer for Scene Text Recognition.

Recent years have witnessed the growing popularity of connectionist temporal classification (CTC) and attention mechanism in scene text recognition (STR). CTC-based methods consume less time with few computational burdens, while they are not as effective as attention-based methods. To retain computational efficiency and effectiveness, we propose the global-local attention-augmented light Transformer (GLaLT), which adopts a Transformer-based encoder-decoder structure to orchestrate CTC and attention mechanism. The encoder integrates the self-attention module with the convolution module to augment the attention, where the self-attention module pays more attention to capturing long-term global dependencies and the convolution module focuses on local context modeling. The decoder consists of two parallel modules: one is the Transformer-decoder-based attention module and the other is the CTC module. The first one is removed in the testing phase and can guide the second one to extract robust features in the training phase. Extensive experiments on standard benchmarks demonstrate that GLaLT achieves state-of-the-art performance for both regular and irregular STR. In terms of tradeoffs, the proposed GLaLT is at or near the frontiers for maximizing speed, accuracy, and computational efficiency at the same time.

Explainability of Speech Recognition Transformers via Gradient-Based Attention Visualization

In vision Transformers, attention visualization methods are used to generate heatmaps highlighting the class-corresponding areas in input images, which offers explanations on how the models make predictions. However, it is not so applicable for explaining automatic speech recognition (ASR) Transformers. An ASR Transformer makes a particular prediction for every input token to form a sentence, but a vision Transformer only makes an overall classification for the input data. Therefore, traditional attention visualization methods may fail in ASR Transformers. In this work, we propose a novel attention visualization method in ASR Transformers and try to explain which frames of the audio result in the output text. Inspired by the model explainability, we also explore ways of improving the effectiveness of the ASR model. Comparing with other Transformer attention visualization methods, our method is more efficient and intuitively understandable, which unravels the attention calculation from information flow of Transformer attention modules. In addition, we demonstrate the utilization of visualization result in three ways: (1) We visualize attention with respect to connectionist temporal classification (CTC) loss to train an ASR model with adversarial attention erasing regularization, which effectively decreases the word error rate (WER) of the model and improves its generalization capability. (2) We visualize the attention on some specific words, interpreting the model by effectively demonstrating the semantic and grammar relationships between these words. (3) Similarly, we analyze how the model manage to distinguish homophones, using contrastive explanation with respect to homophones.

La reconnaissance de l'écriture pour les manuscrits documentaires du Moyen Âge

Handwritten Text Recognition (HTR) techniques aim to accurately recognize sequences of characters in input manuscript images by training artificial intelligence models to capture historical writing features. Efficient HTR models can transform digitized manuscript collections into indexed and quotable corpora, providing valuable research insight for various historical inquiries. However, several challenges must be addressed, including the scarcity of relevant training corpora, the consequential variability introduced by different scribal hands and writing scripts, and the complexity of page layouts. This paper presents two models and one cross-model approach for automatic transcription of Latin and French medieval documentary manuscripts, particularly charters and registers, written between the 12th and 15th centuries and classified into two major writing scripts: Textualis (from the late-11th to 13th century) and Cursiva (from the 13th to the 15th century). The architecture of the models is based on a Convolutional Recurrent Neural Network (CRNN) coupled with a Connectionist Temporal Classification (CTC) loss. The training and evaluation of the models, involving 120k lines of text and almost 1M tokens, were conducted using three available ground-truth corpora : The e-NDP corpus, the Alcar-HOME database and the Himanis project. This paper describes the training architecture and corpora used, while discussing the main training challenges, results, and potential applications of HTR techniques on medieval documentary manuscripts.