Automated Error Detection in Digitized Cultural Heritage Documents

International audienceThe work reported in this paper aims at performance optimization in the digitization of documents pertaining to the cultural heritage domain. A hybrid method is roposed, combining statistical classification algorithms and linguistic knowledge to automatize post-OCR error detection and correction. The current paper deals with the integration of linguistic modules and their impact on error detection

Generating a training corpus for OCR post-correction using encoder-decoder model

International audienceIn this paper we present a novel approach to the automatic correction of OCR-induced orthographic errors in a given text. While current systems depend heavily on large training corpora or exter- nal information, such as domain-specific lexicons or confidence scores from the OCR process, our system only requires a small amount of relatively clean training data from a representative corpus to learn a character-based statistical language model using Bidirectional Long Short- Term Memory Networks (biLSTMs). We demonstrate the versatility and adaptability of our system on different text corpora with varying degrees of textual noise, in- cluding a real-life OCR corpus in the med- ical domain

Hybrid model of post-processing techniques for Arabic optical character recognition

Optical character recognition (OCR) is used to extract text contained in an image. One of the stages in OCR is the post-processing and it corrects the errors of OCR output text. The OCR multiple outputs approach consists of three processes: differentiation, alignment, and voting. Existing differentiation techniques suffer from the loss of important features as it uses N-versions of input images. On the other hand, alignment techniques in the literatures are based on approximation while the voting process is not context-aware. These drawbacks lead to a high error rate in OCR. This research proposed three improved techniques of differentiation, alignment, and voting to overcome the identified drawbacks. These techniques were later combined into a hybrid model that can recognize the optical characters in the Arabic language. Each of the proposed technique was separately evaluated against three other relevant existing techniques. The performance measurements used in this study were Word Error Rate (WER), Character Error Rate (CER), and Non-word Error Rate (NWER). Experimental results showed a relative decrease in error rate on all measurements for the evaluated techniques. Similarly, the hybrid model also obtained lower WER, CER, and NWER by 30.35%, 52.42%, and 47.86% respectively when compared to the three relevant existing models. This study contributes to the OCR domain as the proposed hybrid model of post-processing techniques could facilitate the automatic recognition of Arabic text. Hence, it will lead to a better information retrieval

OCR Post Correction for Endangered Language Texts

There is little to no data available to build natural language processing models for most endangered languages. However, textual data in these languages often exists in formats that are not machine-readable, such as paper books and scanned images. In this work, we address the task of extracting text from these resources. We create a benchmark dataset of transcriptions for scanned books in three critically endangered languages and present a systematic analysis of how general-purpose OCR tools are not robust to the data-scarce setting of endangered languages. We develop an OCR post-correction method tailored to ease training in this data-scarce setting, reducing the recognition error rate by 34% on average across the three languages.Comment: Accepted to EMNLP 202

Automated Error Detection in Digitized Cultural Heritage Documents

International audienceThe work reported in this paper aims at performance optimization in the digitization of documents pertaining to the cultural heritage domain. A hybrid method is roposed, combining statistical classification algorithms and linguistic knowledge to automatize post-OCR error detection and correction. The current paper deals with the integration of linguistic modules and their impact on error detection

Arabic text recognition of printed manuscripts. Efficient recognition of off-line printed Arabic text using Hidden Markov Models, Bigram Statistical Language Model, and post-processing.

Arabic text recognition was not researched as thoroughly as other natural languages. The need for automatic Arabic text recognition is clear. In addition to the traditional applications like postal address reading, check verification in banks, and office automation, there is a large interest in searching scanned documents that are available on the internet and for searching handwritten manuscripts. Other possible applications are building digital libraries, recognizing text on digitized maps, recognizing vehicle license plates, using it as first phase in text readers for visually impaired people and understanding filled forms. This research work aims to contribute to the current research in the field of optical character recognition (OCR) of printed Arabic text by developing novel techniques and schemes to advance the performance of the state of the art Arabic OCR systems. Statistical and analytical analysis for Arabic Text was carried out to estimate the probabilities of occurrences of Arabic character for use with Hidden Markov models (HMM) and other techniques. Since there is no publicly available dataset for printed Arabic text for recognition purposes it was decided to create one. In addition, a minimal Arabic script is proposed. The proposed script contains all basic shapes of Arabic letters. The script provides efficient representation for Arabic text in terms of effort and time. Based on the success of using HMM for speech and text recognition, the use of HMM for the automatic recognition of Arabic text was investigated. The HMM technique adapts to noise and font variations and does not require word or character segmentation of Arabic line images. In the feature extraction phase, experiments were conducted with a number of different features to investigate their suitability for HMM. Finally, a novel set of features, which resulted in high recognition rates for different fonts, was selected. The developed techniques do not need word or character segmentation before the classification phase as segmentation is a byproduct of recognition. This seems to be the most advantageous feature of using HMM for Arabic text as segmentation tends to produce errors which are usually propagated to the classification phase. Eight different Arabic fonts were used in the classification phase. The recognition rates were in the range from 98% to 99.9% depending on the used fonts. As far as we know, these are new results in their context. Moreover, the proposed technique could be used for other languages. A proof-of-concept experiment was conducted on English characters with a recognition rate of 98.9% using the same HMM setup. The same techniques where conducted on Bangla characters with a recognition rate above 95%. Moreover, the recognition of printed Arabic text with multi-fonts was also conducted using the same technique. Fonts were categorized into different groups. New high recognition results were achieved. To enhance the recognition rate further, a post-processing module was developed to correct the OCR output through character level post-processing and word level post-processing. The use of this module increased the accuracy of the recognition rate by more than 1%.King Fahd University of Petroleum and Minerals (KFUPM

Aplicaciones de los autómatas transductores finitos con pesos (WFST) en la corrección simbólica en interfaces persona-máquina

[EN] In this thesis a real application related to the string correction process from an OCR classifier in a form digitizing task is presented. These strings come from a classifier with a given error ratio that implies that some characters in the string have been potentially misclassified, producing erroneous words. This raises the need to introduce some kind of postprocess to improve the strings. The implementation of such postprocess takes into account all the available evidence in a given moment. In the case proposed here, these evidences are the charactersrecognized by the classifier with their posterior probabilities, the confusion matrix between symbols and the language model finally accepted. Each evidence is modelled independently by means of a WFST and then combined by means of the composition operation into a single integrated automata. From this automata, the path that maximizes the probability is selected. This path is the string, that belongs to the language model, that is the nearest string to the OCR hypothesis according to the confusion matrix. The final system offers two different results: on the one hand the corrected string, on the other hand the transformation cost produced during the string correction. Additionally, it is proposed a general method of error estimation using the input string transformation cost that establishes a threshold in terms of the cost and the proposed end-user parameter: the acceptable final error. This thesis presents a method for estimating adaptive rejection threshold estimation that allows for a certain percentage of error in a lot of strings from one language (sample) that presents several advantages. On the one hand, it is independent from transformation cost postprocessing distribution of such samples. On the other hand, it allows the user to set the threshold for a familiar and advantageous manner, as is setting the desired rate of sampling error. For this, first, and for a given language, a model that estimates the probability of error associated with the acceptation of postprocessed strings with a given transformation cost is defined. Then, the procedure that performs the rejection threshold estimation adaptively in order to achieve predefined rate error for a test batch is presented. In addition, an approach to obtain the above model is proposed when there are no real and supervised OCR hypothesis in the learning stage. The chapter is accompanied by experiments whose results demonstrate the utility of the proposed method. Next, linking in somehow with the search for an increased productivity in a possible string validation task, of previously strings rejected by the system through the foregoing error estimation method, a method of multimodal and interactive human-computer interaction that composes the above information with the prefix introduced by the user, while the validation process occurs, making use, for this, of WFST and the automata composition operation. The search for the most likely string for each new interaction offered by the user, in the composed automata, presented here, shows a clear increase in productivity by requiring fewer keystrokes in obtaining the correct string. Finally, a tolerant fault multimodal and interactive interface, using also WFST, is shown by making the composition of different information sources together with an error model related with the possible confusion caused due to the arrangement of keys on a keyboard. The application shown in this case is related to the introduction of a destination into a GPS device where is considered both the information related to the next destinations to a specific place, such as the information related to the entered prefix and errors that may occur due to the arrangement of keys on the input device considered.[ES] En esta tesis se presenta inicialmente una aplicación real de corrección de cadenas procedentes de un clasificador OCR en una tarea de digitalización de formularios. Estas cadenas, proceden de un clasificador con cierta probabilidad de error, lo que implica la posibilidad de que alguno de los caracteres pertenecientes a una palabra sea erróneo, produciendo finalmente palabras incorrectas. Esto plantea la necesidad de introducir algún tipo de postproceso que mejore dichas cadenas. Para implementar dicho postproceso, se tienen en cuenta todas las evidencias disponibles en un momento dado. En el caso propuesto aquí serán los caracteres reconocidos por el propio clasificador con su probabilidad a posteriori, la matriz de confusión entre símbolos y el modelo de lenguaje finalmente aceptado. Cada una de estas evidencias se modela de manera independiente en forma de un WFST. Una vez modeladas se fusionan mediante la operación de composición de autómatas en un único autómata integrado. A partir de este autómata, se selecciona el camino que maximiza la probabilidad y que corresponde con la cadena perteneciente al lenguaje más cercana a la hipótesis OCR según la matriz de confusión entre símbolos. El sistema final ofrecerá dos resultados diferentes: por una parte la cadena corregida y por otra el coste de transformación de dicha corrección. Por otra parte, se plantea un método general de estimación del error frente a un coste de transformación de las cadenas de entrada que permite establecer un umbral dinámico en función de dicho coste y un parámetro propuesto por el usuario final: el error final asumible. Para ello en esta tesis se presenta un método adaptativo de estimación del umbral de rechazo que permite estimarlo para obtener un determinado porcentaje de error en un lote de cadenas de un lenguaje (muestra) que presenta diversas ventajas. Por un lado, es independiente de la distribución de los costes de transformación de dichas muestras. Por otro lado, permite al usuario establecer el umbral de una manera familiar y ventajosa, como es fijando la tasa de error deseada de la muestra. Para todo ello, en primer lugar, y para un lenguaje dado, se define un modelo que estima la probabilidad de error asociada a aceptar cadenas con un coste de transformación determinado. A continuación, se expone el procedimiento que lleva a cabo la estimación del umbral de rechazo de manera adaptativa con el objetivo de alcanzar la tasa de error predefinida para un lote de test. Además, se propone una aproximación para la obtención del modelo anterior cuando no se dispone de hipótesis OCR reales y supervisadas en la etapa de aprendizaje. Seguidamente y enlazando en cierta forma con la búsqueda de un incremento de productividad en una posible validación de las cadenas, previamente rechazadas por el sistema a través del método de estimación del error anteriormente expuesto, se presenta un método de interacción persona-máquina multimodal e interactivo que fusiona la información anterior junto al prefijo introducido, por el propio usuario, durante dicho proceso de validación, haciendo uso para ello de los WFST y la operación de composición de autómatas. Para finalizar, se muestra otra interfaz multimodal e interactiva tolerante a fallos, mediante la fusión de diferentes fuentes de información junto a un modelo de error relacionado con las posibles confusiones producidas debido a la disposición de las teclas de un teclado. Para ello, se hace uso también de WFST para su modelado. La aplicación mostrada en este caso está relacionada con la introducción de un destino en un dispositivo GPS y en ella se considera, tanto la información de los destinos próximos a un lugar concreto, como la información relativa al prefijo introducido y los errores que pueden aparecer debido a la propia disposición de las teclas en el dispositivo de entrada.[CA] En aquesta tesi es presenta inicialment una aplicació real de correcció de cadenes procedents d'un classificador OCR en una tasca de digitalització de formularis. Aquestes cadenes, procedeixen d'un classificador amb una determinada probabilitat d'error, la qual cosa implica la possibilitat de que algun dels caràcters que pertanyen a una paraula siga erroni, produint finalment paraules incorrectes. Això planteja la necessitat d'introduir algun tipus de postprocés que millore aquestes cadenes. Per implementar aquest postprocés, es tenen en compte totes les evidències disponibles en un moment donat. En el cas proposat ací, seran els caràcters reconeguts pel propi classificador amb la seua probabilitat a posteriori, la matriu de confusió entre símbols i el model de llenguatge finalment acceptat. Cadascuna d'aquestes evidències es modela de manera independent en forma d'un WFST. Una vegada modelades es fusionen mitjançant l'operació de composició d'autòmats en un únic autòmat integrat. A partir d'aquest autòmat, es selecciona el camí que fa màxima la probabilitat i que es correspon amb la cadena més propera a la hipòtesi OCR que pertany al llenguatge segons la matriu de confusió entre símbols. El sistema final oferirà dos resultats diferents: d'una banda la cadena corregida, i d'una altra, el cost de transformació d'aquesta correcció. D'una altra banda, es planteja un mètode general d'estimació de l'error front al cost de transformació de les cadenes d'entrada que permet establir un llindar dinàmic en funció d'aquest cost i un paràmetre proposat per l'usuari final: l'error final assumible. Per això en aquesta tesi es presenta un mètode adaptatiu d'estimació de rebuig, amb la finalitat d'obtindre un determinat percentatge d'error en un lot de cadenes d'un llenguatge (mostra) que presenta diversos avantatges. D'una banda és independent de la distribució dels costos de transformació de les mostres esmentades. D'altra banda, permet l'usuari establir el llindar d'una manera familiar i avantatjosa, com és fixant la tasa d'error desitjada per la mostra. Per tot això, en primer lloc, i donat un llenguatge, es defineix un model que estima la probabilitat d'error associada a acceptar cadenes amb un cost de transformació determinat. A continuació, s'exposa el procediment que du a terme l'estimació del llindar de rebuig de manera adaptativa amb l'objectiu de arribar a la tasa d'error predefinida per a un lot de test. A més a més, es proposa una aproximació per a obtindre el model anterior quant no es disposa d'hipòtesi OCR reals i supervisades a l'etapa d'aprenentatge. Seguidament, i enllaçant amb la recerca d'un increment en la productivitat en una possible validació de cadenes prèviament rebutjades pel sistema a través del mètode d'estimació de l'error anteriorment exposat, es presenta un mètode d'interacció persona-màquina multimodal i interactiu que fusiona la informació anterior, juntament amb el prefix introduït pel propi usuari durant l'esmentat procés de validació, fent ús dels WFST i l'operació de composició d'autòmats. La recerca de la cadena més probable, en cada nova interacció oferida per l'usuari ens mostra un clar increment de la productivitat, al requerir un nombre menor de pulsacions de teclat per obtindre la cadena correcta. Per finalitzar, es mostra una altra interfície multimodal i interactiva tolerant a errades, mitjançant la fusió de diferents fonts d'informació juntament a un model d'error relacionat amb les possibles confusions produïdes a causa de la disposició de les lletres d'un teclat. En aquest cas es fa ús també dels WFST en el seu modelat. L'aplicació mostrada en aquest cas està relacionada amb la introducció d'una destinació en un dispositiu GPS i en aquesta es considera tant la informació pròxima a un lloc concret, com la informació relativa al prefix introduït, junt als errors que poden aparèixer a causa de la pròpia disposNavarro Cerdán, JR. (2016). Aplicaciones de los autómatas transductores finitos con pesos (WFST) en la corrección simbólica en interfaces persona-máquina [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/62688TESI