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PDF to Word OCR進入發燒排行的影片

衣索比亞語的文字辨識利用深度學習法

為了解決PDF to Word OCR的問題，作者Direselign Addis Tadesse 這樣論述：

Text is a collection of words or letters which represent a language and one of the most significant innovations of humans. It plays a vital role in human life including communicating ideas and delivering information. These texts are found in different forms such as in handwritten, machine printed,

and electronically editable forms. Texts used in handwritten and machine printed forms must be transcribed into machine editable text that is used for further study, such as text mining, pattern recognition, computer vision, and other applications.For several decades, researchers have been studying

text recognition system also known as optical character recognition (OCR) system. Currently, there are several proprietary systems which efficiently converts simple machine printed scanned image into editable text. However, these systems failed to recognize texts from camera capture natural images a

nd handwritten scanned images. Because, texts found in natural images and handwritten images have a large variability appearance compared to machine printed scanned images. Besides, under resource scripts like Ethiopic script are also another challenge. To address this gap, we propose robust deep le

arning techniques to recognize text from scanned handwritten images and camera captured natural images.This dissertation has two folds. The first focuses on offline handwriting Ethiopic text recognition and the second focuses on scene text detection and recognition. For offline handwriting text reco

gnition, we propose two methods which recognize at character and word/text-line level. To recognize an isolated Ethiopic character, Convolutional Neural Network (CNN) and Multilayer Perceptron (MLP) methods are employed. Additionally, the effects of five optimizers, number of layers and structure ar

e analyzed. The experiment results show that CM4 architecture with AdaGrad optimizer shows better recognition performance than others.The second method uses Gated CNN and Transformer network to recognize Ethiopic text at word and text-line level from offline handwritten scanned images. Compared to c

onventional stack of CNN, stack of Gated CNN and CNN has better performance to extract features. In addition, Transformer network enhances the limitations of recurrent based networks by avoiding recursion. To train and test the proposed model, we prepare a word and text-line database. In addition, w

e introduce a semi-automatic labeling algorithm for word based database preparation. The experiment result of the proposed model shows promising result.On the other hand, a Convolutional Neural Network (CNN) with bidirectional Long Short-Term Memory (LSTM) and Connectionist Temporal Classification (

CTC) also known as Convolutional Recurrent Neural Network (CRNN) is used to recognize Ethiopic scene texts from cropped natural images. The architecture has three layers which are feature extraction layer using stack of CNN, prediction layer using LSTM and loss calculation and transcription layer us

ing CTC. The experiment result shows a promising result. Besides, for bilingual scene text detection as well as for end-to-end scene text reading, octave based feature extractor and time-restricted self-attention encoder-decoder method is used. In the architecture, we use Feature Pyramid Network (F

PN) with octave based ResNet-50 to extract features. The outputs from the feature extraction layer are used to detect text/non-text regions using Region Proposal Network (RPN). Finally, a time-restricted self-attention encoder-decoder method is employed to recognize texts from regions which have tex

t. The experiment result shows that the detection performance on both languages doesn’t have a difference. However, the recognition performance shows that English words are better recognition performance than Amharic words.To evaluate and train both the proposed models, we prepare appropriate databa

ses (i.e. isolated offline handwritten Ethiopic character and numeric database, Ethiopic offline handwritten word and text-line database, real and synthetic bilingual scene text database). In addition, other well-known datasets including ICDAR2013, ICDAR2015, and Total-Text are employed for scene te

xt detection and recognition. The experiment results show a better recognition performance than previously proposed methods.