Tetap teratur dengan koleksi
Simpan dan kategorikan konten berdasarkan preferensi Anda.
Ringkasan Document AI
Dokumen ini adalah panduan konsep dasar penggunaan Document AI.
Anda harus membaca halaman ini sebelum melanjutkan ke dokumentasi atau panduan memulai lainnya.
Mengotomatiskan alur kerja pemrosesan dokumen
Bisnis di seluruh dunia sangat mengandalkan dokumen untuk menyimpan dan menyampaikan informasi.
Informasi ini sering kali perlu didigitalkan agar berguna. Namun,
hal ini biasanya dilakukan melalui proses manual yang memakan waktu.
Contoh:
Mendigitalkan buku untuk pembaca elektronik.
Memproses formulir masukan medis di klinik dokter.
Mengurai tanda terima dan invoice untuk validasi laporan pengeluaran.
Mengautentikasi identitas berdasarkan kartu tanda pengenal.
Mengekstrak informasi penghasilan dari formulir pajak untuk menyetujui pinjaman.
Memahami kontrak untuk persyaratan perjanjian bisnis utama.
Setiap alur kerja ini melibatkan mendapatkan teks mentah dari dokumen, lalu
mengekstrak teks tertentu dari teks yang sesuai dengan data yang diperlukan (kolom atau entitas).
Namun, setiap jenis dokumen memiliki struktur dan tata letak yang berbeda, dan pola kolom
bervariasi bergantung pada kasus penggunaan tertentu.
Komponen Document AI
Document AI adalah platform pemahaman dan pemrosesan dokumen
yang mengambil data tidak terstruktur dari dokumen dan mengubahnya menjadi
data terstruktur (kolom tertentu, cocok untuk database), sehingga lebih mudah dipahami, dianalisis, dan digunakan.
Document AI dibuat berdasarkan produk dalam Vertex AI dengan AI generatif untuk membantu Anda
membuat aplikasi pemrosesan dokumen berbasis cloud yang skalabel dan menyeluruh tanpa keahlian machine learning khusus.
Dengan Document AI, Anda dapat:
Digitalkan dokumen menggunakan OCR untuk mendapatkan teks, tata letak, dan berbagai add-on seperti deteksi kualitas gambar (untuk keterbacaan) dan penghapusan kemiringan (otomatis sepenuhnya).
Ekstrak informasi teks dan tata letak, dari file dokumen dan normalisasi entitas.
Mengidentifikasi pasangan nilai kunci (kvp) dalam formulir terstruktur dan tabel reguler. Misalnya: Name: Jill Smith adalah kvp.
Klasifikasikan jenis dokumen untuk mendorong proses downstream seperti ekstraksi dan penyimpanan.
Memisahkan dan mengklasifikasikan dokumen menurut jenisnya. Misalnya, file PDF dengan beberapa dokumen asli.
Siapkan set data untuk digunakan dalam penyesuaian dan evaluasi model menggunakan fitur pemberian label otomatis,
pengelolaan skema, dan pengelolaan set data seperti peninjauan dokumen dan prediksi.
Integrasikan dengan produk seperti Cloud Storage, BigQuery, dan Vertex AI Search
untuk membantu Anda menyimpan, menelusuri, mengatur, mengatur, dan menganalisis dokumen dan metadata.
Diagram ini menggambarkan semua langkah pemrosesan dokumen utama yang
didukung oleh Document AI dan cara langkah-langkah tersebut dapat terhubung satu sama lain.
Prosesor
Pemroses Document AI berada di antara file dokumen dan model
machine learning yang melakukan tindakan pemahaman dan pemrosesan dokumen.
Model ini dapat digunakan untuk mengklasifikasikan, memisahkan, mengurai, atau menganalisis dokumen.
Setiap Google Cloud project perlu membuat instance pemrosesnya sendiri.
Prosesor termasuk dalam salah satu kategori berikut:
Digitalisasi: OCR.
Extract: Pengekstrak kustom, Form Parser, parser tata letak, dan parser terlatih.
Klasifikasikan: Pengklasifikasi kustom dan pemisah kustom.
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Sulit dipahami","hardToUnderstand","thumb-down"],["Informasi atau kode contoh salah","incorrectInformationOrSampleCode","thumb-down"],["Informasi/contoh yang saya butuhkan tidak ada","missingTheInformationSamplesINeed","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Lainnya","otherDown","thumb-down"]],["Terakhir diperbarui pada 2025-09-02 UTC."],[[["\u003cp\u003eDocument AI is a platform that transforms unstructured data from documents into structured data, making it easier to understand, analyze, and use.\u003c/p\u003e\n"],["\u003cp\u003eDocument AI enables the automation of document processing workflows, such as digitizing documents, extracting text and entities, classifying document types, and preparing datasets for model training.\u003c/p\u003e\n"],["\u003cp\u003eDocument AI uses processors that fall into the categories of digitize, extract, or classify to perform specific document processing and understanding actions.\u003c/p\u003e\n"],["\u003cp\u003eTo use Document AI, you must choose a suitable processor, create the processor, optionally train it, and then send documents to the processor for processing.\u003c/p\u003e\n"],["\u003cp\u003eDocument AI can integrate with products like Cloud Storage, BigQuery, and Vertex AI Search for storing, searching, and analyzing documents.\u003c/p\u003e\n"]]],[],null,["# Document AI overview\n====================\n\nThis document is a guide to the fundamental concepts of using Document AI.\nYou should read this page before proceeding to any other documentation or quickstarts.\n\nAutomate document processing workflows\n--------------------------------------\n\nBusinesses all over the world rely heavily on documents to store and convey information.\nThis information often needs to be digitized for it to become useful. However,\nthis is usually accomplished through time-intensive, manual processes.\n\nFor example:\n\n- Digitizing books for e-readers.\n- Processing medical intake forms at doctor's offices.\n- Parsing receipts and invoices for expense report validation.\n- Authenticating identity based on ID cards.\n- Extracting income information from tax forms for approving loans.\n- Understanding contracts for key business agreement terms.\n\nEach of these workflows involve getting the raw text from documents, then\nextracting specific text from that which corresponds to the data needed (the fields or entities).\nHowever, each document type has a different structure and layout, and the pattern of fields\nvary depending on the specific use case.\n\nDocument AI components\n----------------------\n\nDocument AI is a [document processing and understanding](https://en.wikipedia.org/wiki/Document_processing)\nplatform that takes unstructured data from documents and transforms it into\nstructured data (specific fields, suitable for a database), making it easier to understand, analyze, and consume.\n\nDocument AI is built on top of products within Vertex AI with generative AI to help you\ncreate scalable, end-to-end, cloud-based document processing applications without specialized machine learning expertise.\n\nUsing Document AI, you can:\n\n- **Digitize documents** using OCR to get text, layout, and various add ons such as image quality detection (for readability) and deskewing (fully automatic).\n- **Extract** text and layout information, from document files and normalize entities.\n- **Identify key-value pairs (kvp)** in structured forms and regular tables. For example: `Name: Jill Smith` is a kvp.\n- **Classify** document types to drive downstream processes such as extraction and storage.\n- **Split** and classify documents by type. For example, a PDF file with multiple real documents.\n- **Prepare datasets** to be used in fine-tuning and model evaluations using auto-labeling, schema management, and dataset management features such as document and prediction review.\n- **Integrate it with products** like Cloud Storage, BigQuery, and Vertex AI Search to help you store, search, organize, govern, and analyze documents and metadata.\n\nThis diagram illustrates all of the key document processing steps that are\nsupported by Document AI and how they can connect to each other.\n\nProcessor\n---------\n\nA Document AI processor lies between the document file and a machine\nlearning model that performs document processing and understanding actions.\nThey can be used to classify, split, parse, or analyze a document.\n\nEach Google Cloud project needs to create its own processor instances.\n\nProcessors fit into one of the following categories:\n\n- **Digitize**: OCR.\n- **Extract**: Custom extractor, Form Parser, layout parser, and pretrained parsers.\n- **Classify**: Custom classifier and custom splitter.\n\nRefer to the [Full processor and detail list](/document-ai/docs/processors-list) for information about all\navailable processor types for Document AI.\n\n### Which processor should I use?\n\nTo decide what processor type to use for a specific application, here are some general guidelines:\n| **Note:** All processors can extract text and layout information.\n\nThis diagram helps determine which processor works best for each use case.\n\n### Use Document AI processors\n\nHere are the major steps to use Document AI to start processing documents:\n\n1. **Choose a processor** that is suitable for your use case.\n\n - For complete information on each processor, see the [Full processor and detail list](/document-ai/docs/processors-list).\n2. **Create a processor** using the Google Cloud console or the Document AI API.\n\n - Document AI creates a **prediction endpoint** where you can send your documents.\n\n - For detailed instructions, see [Creating a processor](/document-ai/docs/create-processor).\n\n3. **Train a processor** with train and test data from scratch, or uptrain a new (pretrained) processor version on top of an existing one.\n\n - For detailed instructions, see [Train processor](/document-ai/docs/workbench/train-processor).\n4. **Send your documents** for processing.\n\n - Document AI processes the documents and returns one or more [`Document`](/document-ai/docs/reference/rest/v1/Document) objects, which contain the extracted, structured information.\n\n - For detailed instructions, see [Sending a processing request](/document-ai/docs/send-request) and [Handle the processing response](/document-ai/docs/handle-response)."]]
