Offline Recognition of Malayalam and Kannada Handwritten Documents Using Deep Learning

For a variety of reasons, handwritten text can be digitalized. It is used in a variety of government entities, including banks, post offices, and archaeological departments. Handwriting recognition, on the other hand, is a difficult task as everyone has a different writing style. There are essentially two methods for handwritten recognition: a holistic and an analytic approach. The previous methods of handwriting recognition are time- consuming. However, as deep neural networks have progressed, the approach has become more straightforward than previous methods. Furthermore, the bulk of existing solutions are limited to a single language. To recognise multilanguage handwritten manuscripts offline, this work employs an analytic approach. It describes how to convert Malayalam and Kannada handwritten manuscripts into editable text. Lines are separated from the input document first. After that, word segmentation is performed. Finally, each word is broken down into individual characters. An artificial neural network is utilised for feature extraction and classification. After that, the result is converted to a word document

Deep Adaptive Learning for Writer Identification based on Single Handwritten Word Images

There are two types of information in each handwritten word image: explicit information which can be easily read or derived directly, such as lexical content or word length, and implicit attributes such as the author's identity. Whether features learned by a neural network for one task can be used for another task remains an open question. In this paper, we present a deep adaptive learning method for writer identification based on single-word images using multi-task learning. An auxiliary task is added to the training process to enforce the emergence of reusable features. Our proposed method transfers the benefits of the learned features of a convolutional neural network from an auxiliary task such as explicit content recognition to the main task of writer identification in a single procedure. Specifically, we propose a new adaptive convolutional layer to exploit the learned deep features. A multi-task neural network with one or several adaptive convolutional layers is trained end-to-end, to exploit robust generic features for a specific main task, i.e., writer identification. Three auxiliary tasks, corresponding to three explicit attributes of handwritten word images (lexical content, word length and character attributes), are evaluated. Experimental results on two benchmark datasets show that the proposed deep adaptive learning method can improve the performance of writer identification based on single-word images, compared to non-adaptive and simple linear-adaptive approaches.Comment: Under view of Pattern Recognitio

Implementasi Support Vector Machine Berdasarkan Ciri Histogram Of Oriented Gradients Untuk Verifikasi Citra Tanda Tangan Berbasis Raspberry PI

Tanda tangan telah lama digunakan oleh masyarakat sebagai salah satu alat untuk verifikasi identitas seseorang, umumnya tanda tangan digunakan dalam dokumen resmi. Sayangnya tanda tangan juga dapat disalahgunakan untuk memalsukan legalitas dokumen. Untuk menghindari penyalahgunaan atau pemalsuan tanda tangan tersebut, maka dibuatlah alat untuk dapat melakukan verifikasi tanda tangan. Sistem ini menggunakan kamera sebagai masukkan dengan push button sebagai pemicu kamera menangkap citra, Raspberry Pi sebagai unit pemroses citra digital, dan LCD 16x2 sebagai keluaran sistem. Penelitian ini menggunakan metode Histogram of Oriented Gradients (HOG) sebagai feature descriptor dan preprocessing citra seperti konversi colorspace citra, Thresholding, Morphological Transformation, dan deteksi dan koreksi kemiringan. Metode HOG tersebut akan menghasilkan feature vector yang merepresentasikan ciri tanda tangan pada citra, feature vector ini selanjutnya akan masuk ke proses klasifikasi Support Vector Machine (SVM) untuk dilakukan pelatihan data dan prediksi keluaran verifikasi citra. Dalam sisi software, terdapat dua bagian utama sistem, yaitu bagian pelatihan data untuk melatih data citra tanda tangan dengan SVM dan bagian verifikasi tanda tangan untuk prediksi keluaran verifikasi, selain itu sistem juga menggunakan bantuan software OpenCV sebagai library pengolahan citra. Dari pengujian akurasi verifikasi citra tanda tangan didapatkan hasil sebesar 87,33% dari data uji sebanyak 300 citra tanda tangan. Dari pengujian ini data latih sangat mempengaruhi akurasi, data latih yang memiliki pola tanda tangan asli yang cukup bervariasi dapat menurunkan akurasi sistem. Dalam pengujian waktu proses pelatihan data klasifikasi, rata-rata sistem membutuhkan 1,45 detik untuk melatih data, dalam setiap pengujiannya waktu pemrosesan tidak terlalu bervariasi, hal ini dikarenakan komputasi program ditangani oleh proses scheduling dari sistem operasi Raspbian