This study presents a large-scale dataset of Khmer scene text images captured in real-world environments. Khmer, the official language of Cambodia, is spoken by approximately 17 million people. While Optical Character Recognition (OCR) systems have achieved remarkable success in Roman (Latin) script languages such as English, Khmer script poses unique challenges due to its intricate structure, absence of clear word boundaries, and highly diverse character shapes and sizes. A significant limitation in Khmer OCR research has been the scarcity of high-quality training data, particularly for deep learning-based models, which require extensive datasets to achieve robust performance. To address these challenges, we introduce a newly constructed dataset of Khmer scene text, comprising 29,601 annotated text lines from 10,000 unique images. This dataset is highly diverse and challenging, encompassing artistic text, blurred text, low-light conditions, curved text, text in complex backgrounds, and occluded text. Each text line is annotated with polygonal bounding box coordinates and line-level transcriptions, alongside attributes describing background complexity, character appearance, and text style. To establish a foundational benchmark for future research in Khmer OCR, we provide baseline results for Khmer text detection and recognition. Additionally, we propose a robust evaluation metric tailored for Khmer OCR, enabling precise assessment of CER and WER while accounting for the unique characteristics of the Khmer script.

In this paper, a novel local threshold binarization method using fast Fuzzy C-Means clustering is proposed. Historical document images with non-uniform background, stains, faded ink are first processed by removing the background using inpainting based method. Then using Fuzzy C-Means clustering is used to cluster out the pixels into three main clusters : sure text pixels, sure background pixels and confused pixels which may or may not be labeled as text. Based on the structural symmetry of pixels (SSP), these confused pixels are then classified into text or background pixels. The SSP is defined as those pixels around strokes whose gradient magnitudes are big enough and whose directions are opposite. As the gradient map is our basis for computing the SSP, we further propose to estimate the background surface first and to extract potential SSP in the compensated image so as to deal with degradations of document images such as uneven illumination, low contrast and stain. To prove the effectiveness of our method, tests on eight public document image datasets are preformed and the experimental results show that our method outperforms other local threshold binarization approaches on both F-measure and PSNR.

The accuracy of Optical Character Recognition (OCR) technologies considerably impacts the way digital documents are indexed, accessed and exploited. Post-processing approaches detect and correct remaining errors to improve the quality of OCR texts. However, state-of-the-art approaches still need to be improved. Most of the existing post-OCR techniques use predefined error position lists or apply simple techniques to detect errors. In this paper, we describe a novel error detector using different features from character-level (including character noisy channel, index of peculiarity) to word-level (such as frequencies of n-grams, skip-grams, part-of-speech) Experimental results show that our approach outperforms the best performing techniques in the ICDAR 2017 Competition on Post-OCR text correction.

This paper describes the second round of the ICDAR 2019 competition on post-OCR text correction and presents the different methods submitted by the participants. OCR has been an active research field for over the past 30 years but results are still imperfect, especially for historical documents. The purpose of this competition is to compare and evaluate automatic approaches for correcting (denoising) OCR-ed texts. The present challenge consists of two tasks: 1) error detection and 2) error correction. An original dataset of 22M OCR-ed symbols along with an aligned ground truth was provided to the participants with 80% of the dataset dedicated to training and 20% to evaluation. Different sources were aggregated and contain newspapers, historical printed documents as well as manuscripts and shopping receipts, covering 10 European languages (Bulgarian, Czech, Dutch, English, Finish, French, German, Polish, Spanish and Slovak). Five teams submitted results, the error detection scores vary from 41 to 95% and the best error correction improvement is 44%. This competition, which counted 34 registrations, illustrates the strong interest of the community to improve OCR output, which is a key issue to any digitization process involving textual data.

In this paper, we describe a method to restore a flow of continuous documents. The flow is a collection of consecutive scanned pages without explicit separation marks between documents. Our method is based on contextual and layout descriptors meant to specify the relationship between each pair of consecutive pages. The relationships are represented using vectors of features with boolean values indicating the presence or the absence of descriptors on concerned pages. The segmentation task therefore consists in classifying such vectors into continuities or breaks. The continuity class indicates that pages belong to the same document while the break class ends the ongoing document and starts a new one. The experimental part is based on a large collection of real administrative documents.

This paper describes the ICDAR2017 competition on post-OCR text correction and presents the different methods submitted by the participants. OCR has been an active research field for over the past 30 years but results are still imperfect, especially for historical documents. The purpose of this competition is to compare and evaluate automatic approaches for correcting (denoising) OCR-ed texts. The challenge consists of two independent tasks: 1) error detection and 2) error correction. An original dataset of 12M OCR-ed symbols along with an aligned ground truth was provided to the participants with 80% of the dataset dedicated to the training and 20% to the evaluation. Different sources were aggregated and namely contain newspapers and monographs covering 2 languages (English and French). 11 teams submitted results, while the difficulty of the task was underlined by the fact that only half of the submitted methods were able to denoise the evaluation dataset on average. In any case, this competition, which counted 35 registrations, illustrates the strong interest of the community in this essential problem, which is key to any digitization process involving textual data.

This article focuses on the specific issue of drop caps image recognition in the context of cultural heritage preservation. Due to their heterogeneity and their weakly structured properties, these historical images represent challenging data. An important aspect in the recognition process of drop caps is their background styles, which can be considered as discriminative features to identify both the printer and the period. Most existing methods for style recognition are based on low-level features such as color or texture properties. In this article, we present a novel framework for the recognition of drop caps style based on features of higher levels. We propose to capture the spatial structure carried by these images using relative position descriptors modeling the enlacement between local cells of pixel layers obtained from a document segmentation step. Such descriptors are then exploited in an efficient bag-of-features learning procedure. Experimental results obtained on a dataset of historical drop caps images highlight the interest of this approach, and in particular the benefit of considering spatial information.

Document forgery is an increasing problem for both the public administration and private companies. It represents substantial losses in time and economical resources. Classical solutions to this problem such as watermarks or other integrated security patterns can not be applied in general for any unknown incoming document due to the large variability on types of documents. In that scenario it is important to resort to forensic techniques to seek and analyze inconsistencies on the intrinsic features of the document image. In this paper we present a classification-based approach for forgery detection. We use uniform Local Binary Patterns (LBP) to capture discriminant texture features that are common on forged regions. Besides, we combine multiple descriptors from neighboring regions to model contextual information. Results using Support Vector Machines (SVM) for patch classification show that we are able to detect several types of forgeries in a wide range of types of documents.

The OCR-ed books usually lack logical structure information, such as chapters, sections. To enrich the navigation experience of users, several approaches have been proposed to extract table of contents (ToC) from digitised books. In this paper, we introduce an aggregation-based method to enhance ToC extraction using system submissions from the ICDAR Book structure extraction competitions (2009, 2011, and 2013). Our experimental results show that the union of two best approaches outperforms the existing approaches using both the title-based and link-based evaluation measures on a dataset of more than 2000 books. By efficiently combining the results of existing systems in an unsupervised way, we consistently beat the state-of-the-art in book structure extraction, with performance improvements that are statistically significant.

In this paper, we propose a new feature vector, named DElaunay TRIangulation-based Features (DETRIF), for real-time camera-based document image retrieval. DETRIF is computed based on the geometrical constraints from each pair of adjacency triangles in delaunay triangulation which is constructed from centroids of connected components. Besides, we employ a hashing-based indexing system in order to evaluate the performance of DETRIF and to compare it with other systems such as LLAH and SRIF. The experimentation is carried out on two datasets comprising of 400 heterogeneous-content complex linguistic map images (huge size, 9800 X 11768 pixels resolution) and 700 textual document images.

Smartphones are enabling new ways of capture, hence arises the need for seamless and reliable acquisition and digitization of documents, in order to convert them to editable, searchable and a more human-readable format. Current state-of-the-art works lack databases and baseline benchmarks for digitizing mobile captured documents. We have organized a competition for mobile document capture and OCR in order to address this issue. The competition is structured into two independent challenges: smartphone document capture, and smartphone OCR. This report describes the datasets for both challenges along with their ground truth, details the performance evaluation protocols which we used, and presents the final results of the participating methods. In total, we received 13 submissions: 8 for challenge-1, and 5 for challenge-2.

This paper proposes a comparison of three classification methods of graphical historical images. Historical image datasets are becoming bigger and bigger, and the use of classical computer vision techniques is not sufficient to deal with these large repositories. In the context of this paper, we propose to compare three methods by applying graph matching techniques on a dataset already used in many papers. The first one is based on a statistical approach, the second one on a graph-based classification, and finally the third one is an hybrid approach relying on the specificities of the two previous one. For this last method, we propose here to adapt it to this specific dataset. Some results are proposed and commented, what shows the superiority of the hybrid approach.

In this paper, we propose a new feature vector, named Scale and Rotation Invariant Features (SRIF), for real-time camera-based document image retrieval. SRIF is based on Locally Likely Arrangement Hashing (LLAH), which has been widely used and accepted as an efficient real-time camera-based document image retrieval method based on text. SRIF is computed based on geometrical constraints between pairs of nearest points around a keypoint. It can deal with feature point extraction errors which are introduced as a result of the camera capturing of documents. The experimental results show that SRIF outperforms LLAH in terms of retrieval accuracy and processing time.

In this paper, we present camera-based document retrieval systems using various local features as well as various indexing methods. We employ our recently developed features, named Scale and Rotation Invariant Features (SRIF), which are computed based on geometrical constraints between pairs of nearest points around a keypoint. We compare SRIF with state-of-the-art local features. The experimental results show that SRIF outperforms the state-of-the-art in terms of retrieval time with 90.8% retrieval accuracy.

This article tackles some important issues relating to the analysis of a particular case of complex ancient graphic images, called “lettrines”, “drop caps”, or “ornamental letters”. Our contribution focuses on proposing generic solutions for lettrine recognition and classification. Firstly, we propose a bottom-up segmentation method, based on texture, ensuring the separation of the letter from the elements of the background in an ornamental letter. Secondly, a structural representation is proposed for characterizing a lettrine. This structural representation is based on filtering automatically relevant information by extracting representative homogeneous regions from a lettrine to generate a graph-based signature. The proposed signature provides a rich and holistic description of the lettrine style by integrating varying low-level features (e.g. texture). Then, to categorize and classify lettrines with similar style, structure (i.e. ornamental background) and content (i.e. letter), a graph-matching paradigm has been carried out to compare and classify the resulting graph-based signatures. Finally, to demonstrate the robustness of the proposed solutions and provide additional insights into their accuracies, an experimental evaluation has been conducted using a relevant set of lettrine images. In addition, we compare the results achieved with those obtained using the state-of-the-art methods to illustrate the effectiveness of the proposed solutions.

The document digitization process becomes a crucial economical issue in our society. Then, it becomes necessary to be able to organize this huge amount of documents. The work proposed in this paper tends to propose a new method to automatically classify document using a saliency-based segmentation process on one hand, and a terminology extraction and annotation on the other hand. The saliency-based segmentation is used to extract salient regions and by the way logo, while the terminology approach is used to annotate them and to automatically classify the document. The approach does not require human expertise, and use Google Images as a knowledge database. The results obtained on a real database of 1766 documents show the relevance of the approach.

This paper deals with cultural heritage preservation and ancient document indexing. In the management of historical documents, ancient images are described using semantic information, often manually annotated by historians. In this paper, we propose an approach to interactively propagate the historians' knowledge to a database of drop caps images manually populated by historians with drop caps image annotations. Based on a novel document indexing processing scheme which combines the use of the Zipf law and the use of bag of patterns, our approach extends the Bag of Words model to represent the knowledge by visual features through relevance feedback. Then annotation propagation is automatically performed to propagate knowledge to the drop caps image database. In this article, our approach is presented together with preliminary experimental results and an illustrative example.

To reduce the gap between pixel data and thesaurus semantics, this paper presents a novel approach using mapping between two ontologies on images of drop-capitals (also named drop caps or lettrines): In the first ontology, each drop cap image is endowed with semantic information describing its content. It is generated from a database of lettrines images - namely Ornamental Letter Images Data Base - manually populated by historians with drop cap images annotations. For the second ontology we have developed image processing algorithms to extract image regions on the basis of a number of features. These features, as well as spatial relations, among regions form the basis of the ontology. The ontologies are then enriched by inference rules to annotate some regions to automatically deduce their semantics. In this article, the method is presented together with preliminary experimental results and an illustrative example.

Based on the principle described by Pareti et al. in [1], [2], and by Chouaib et al. in [3], this paper proposes to combine the use of the Zipf law and the use of bag of patterns for the implementation of a document indexing processing scheme. Contrarily to these two mentioned approaches, we retain the most important patterns based on the TF-IDF criteria, and the pattern selection is local. This paper presents the different stages of our indexing process, as well as their application to historical documents. Results on comlex images are given, illustrated and discussed.

This paper proposes an approach to process drop cap images - images of decorated letter that begin chapters of old documents that are preserved in libraries, museums - in the domain of characterization, classification and indexing of old documents. The originality of our proposal is based on the fact that we do not try to extract the letter of drop caps but to classify the drop caps according to period, author and style. The drop caps are characterized by using relevant visual features such as length, thickness, orientation, complexity and change of direction on their primitive elements: strokes. The purpose of this approach is to efficiently extract information embedded in the drop caps for the classification and the indexing of old documents. These new visual features based on bags of strokes are more easily calculable and generally applicable than texture or shape features. Experiments based on characterization, classification and indexing phases demonstrate the performance of our propositions and the advances that they represent in terms of content-based drop caps retrieval.

This paper present a new method to extract shapes in drop caps and particularly the most important shape: letter itself. This method relies on a combination of a Aujol and Chambolle algorithm first, and a segmentation using a Zipf law in a second step. This method can be enhanced as a three-step process: 1) decomposition in layers 2) segmentation using a Zipf law 3) selection of connected components to only emphasize the required information - letter itself.