Tetap teratur dengan koleksi
Simpan dan kategorikan konten berdasarkan preferensi Anda.
Dengan API pengenalan tinta digital ML Kit, Anda dapat mengenali teks tulisan tangan dan mengklasifikasikan gestur pada platform digital dalam ratusan bahasa, serta mengklasifikasikan sketsa. API pengenalan tinta digital menggunakan teknologi yang sama yang
mendukung pengenalan tulis tangan di Gboard, Google Terjemahan, dan
game Quick, Draw!.
Pengenalan tinta digital memungkinkan Anda untuk:
Tulis di layar alih-alih mengetik di keyboard virtual. Hal ini memungkinkan pengguna
menggambar karakter yang tidak tersedia di keyboard mereka, seperti ệ, अ, atau 森
untuk keyboard alfabet latin.
Melakukan operasi teks dasar (navigasi, pengeditan, pemilihan, dan sebagainya)
menggunakan gestur.
Mengenali bentuk dan emoji yang digambar dengan tangan.
Pengenalan tinta digital berfungsi dengan goresan yang digambar pengguna di layar. Jika
Anda perlu membaca teks dari gambar yang diambil dengan kamera, gunakan
Text Recognition API.
Pengenalan tinta digital berfungsi sepenuhnya secara offline serta didukung di Android dan iOS.
Menjaga penyimpanan di perangkat tetap rendah dengan mendownload paket bahasa secara dinamis sesuai
kebutuhan
Pengenal mengambil objek Ink sebagai input. Ink adalah representasi vektor dari apa yang telah ditulis pengguna di layar: urutan goresan, masing-masing berupa daftar koordinat dengan informasi waktu yang disebut titik sentuh. Goresan
dimulai saat pengguna meletakkan stilus atau jarinya ke bawah dan berakhir saat mengangkatnya. Ink diteruskan ke pengenal, yang menampilkan satu atau beberapa kemungkinan hasil pengenalan, dengan tingkat keyakinan.
Contoh
Tulisan tangan bahasa Inggris
Gambar di sebelah kiri di bawah menunjukkan apa yang digambar pengguna pada layar. Gambar di sebelah kanan adalah objek Ink yang sesuai. Objek ini berisi goresan dengan titik merah yang mewakili titik sentuh dalam setiap goresan.
Ada empat garis. Dua goresan pertama dalam objek Ink terlihat seperti ini:
Tinta
Goresan 1
x
392, 391, 389, 287, ...
y
52, 60, 76, 97, ...
t
0, 37, 56, 75, ...
Goresan 2
x
497, 494, 493, 490, ...
y
167, 165, 165, 165, ...
t
694, 742, 751, 770, ...
...
Saat Anda mengirimkan Ink ini ke pengenal bahasa Inggris, kode ini akan menampilkan
beberapa kemungkinan transkripsi, yang berisi lima atau enam karakter. Data tersebut diurutkan dengan mengurangi keyakinan:
RecognitionResult
Kandidat Pengakuan #1
Handw
Kandidat Pengenalan #2
Handrw
Kandidat Pengenalan #3
hardw
Kandidat Pengenalan #4
Handu
Kandidat Pengakuan #5
Handwe
Gestur
Pengklasifikasi gestur mengklasifikasikan goresan tinta ke dalam salah satu dari sembilan class gestur
yang tercantum di bawah ini.
Gestur
Contoh
arch:above arch:below
caret:above caret:below
circle
corner:downleft
scribble
strike
verticalbar
writing
Sketsa emoji
Gambar di sebelah kiri di bawah menunjukkan apa yang digambar pengguna pada layar. Gambar di sebelah kanan adalah objek Ink yang sesuai. Objek ini berisi goresan dengan titik merah yang mewakili titik sentuh dalam setiap goresan.
Objek Ink berisi enam goresan.
Tinta
Goresan 1
x
269, 266, 262, 255, ...
y
40, 40, 40, 41, ...
t
0, 36, 56, 75, ...
Goresan 2
x
179, 182, 183, 185, ...
y
157, 158, 159, 160, ...
t
2475, 2522, 2531, 2541, ...
...
Saat mengirim Ink ini ke pengenal emoji, Anda akan mendapatkan beberapa kemungkinan
transkripsi, yang diurutkan dengan mengurangi keyakinan:
[null,null,["Terakhir diperbarui pada 2025-07-25 UTC."],[[["\u003cp\u003eML Kit's Digital Ink Recognition API recognizes handwritten text and gestures, converting them into digital format, comparable to the technology used in Gboard and Google Translate.\u003c/p\u003e\n"],["\u003cp\u003eThis API enables on-screen writing in various languages, using gestures for text editing, and recognizing hand-drawn shapes and emojis, all without an internet connection.\u003c/p\u003e\n"],["\u003cp\u003eIt supports over 300 languages and 25+ writing systems, along with gesture classification and emoji recognition, functioning by processing stroke data of user input.\u003c/p\u003e\n"],["\u003cp\u003eDevelopers can integrate this feature to allow users to write with styluses or fingers, replacing or supplementing traditional keyboard input for a more natural and versatile user experience.\u003c/p\u003e\n"]]],["ML Kit's digital ink recognition API converts handwritten text, gestures, and sketches into digital formats. It operates offline on Android and iOS, supporting 300+ languages and 25+ writing systems. The API processes user-drawn strokes (Ink objects) to recognize text, emojis, and basic shapes, returning ranked recognition results. Gestures are classified into nine categories, aiding in text operations and user interface actions. Language packs are dynamically downloaded for space efficiency.\n"],null,["With ML Kit's digital ink recognition API, you can recognize handwritten text\nand classify gestures on a digital surface in hundreds of languages, as well as\nclassify sketches. The digital ink recognition API uses the same technology that\npowers handwriting recognition in Gboard, Google Translate, and the\n[Quick, Draw!](https://quickdraw.withgoogle.com/) game.\n\nDigital ink recognition allows you to:\n\n- Write on the screen instead of typing on a virtual keyboard. This lets users draw characters that are not available on their keyboard, such as ệ, अ or 森 for latin alphabet keyboards.\n- Perform basic text operations (navigation, editing, selection, and so on) using gestures.\n- Recognize hand‑drawn shapes and emojis.\n\nDigital ink recognition works with the strokes the user draws on the screen. If\nyou need to read text from images taken with the camera, use the\n[Text Recognition API](/ml-kit/vision/text-recognition).\n\nDigital ink recognition works fully offline and is supported on Android and iOS.\n\n[iOS](/ml-kit/vision/digital-ink-recognition/ios)\n[Android](/ml-kit/vision/digital-ink-recognition/android)\n\nKey Capabilities\n\n- Converts handwritten text to sequences of unicode characters\n- Runs on the device in near real time\n- The user's handwriting stays on the device, recognition is performed without any network connection\n- Supports 300+ languages and 25+ writing systems, see the [complete list of supported languages](/ml-kit/vision/digital-ink-recognition/base-models#text)\n - Supports gesture classification for these languages via [`-x-gesture` extensions](/ml-kit/vision/digital-ink-recognition/base-models#text)\n- Recognizes emojis and basic shapes\n- Keeps on-device storage low by dynamically downloading language packs as needed\n\nThe recognizer takes an `Ink` object as input. `Ink` is a vector representation\nof what the user has written on the screen: a sequence of *strokes* , each being\na list of coordinates with time information called *touch points* . A stroke\nstarts when the user puts their stylus or finger down and ends when they lift it\nup. The `Ink` is passed to a recognizer, which returns one or more possible\nrecognition results, with levels of confidence.\n\nExamples\n\nEnglish handwriting\n\nThe image on the left below shows what the user drew on the screen. The image on\nthe right is the corresponding `Ink` object. It contains the strokes with red\ndots representing the touch points within each stroke.\n\n\nThere are four strokes. The first two strokes in the `Ink` object look like\nthis:\n\n| **Ink** |||\n|----------|-----|-------------------------|\n| Stroke 1 | `x` | 392, 391, 389, 287, ... |\n| Stroke 1 | `y` | 52, 60, 76, 97, ... |\n| Stroke 1 | `t` | 0, 37, 56, 75, ... |\n| Stroke 2 | `x` | 497, 494, 493, 490, ... |\n| Stroke 2 | `y` | 167, 165, 165, 165, ... |\n| Stroke 2 | `t` | 694, 742, 751, 770, ... |\n| ... | | |\n\nWhen you send this `Ink` to a recognizer for the English language, it returns\nseveral possible transcriptions, containing five or six characters. They are\nordered by decreasing confidence:\n\n| **RecognitionResult** ||\n|-------------------------|--------|\n| RecognitionCandidate #1 | handw |\n| RecognitionCandidate #2 | handrw |\n| RecognitionCandidate #3 | hardw |\n| RecognitionCandidate #4 | handu |\n| RecognitionCandidate #5 | handwe |\n\nGestures\n\nGesture classifiers classify an ink stroke into one of nine gesture classes\nlisted below.\n\n| Gesture | Example |\n|-----------------------------|---------|\n| `arch:above` `arch:below` | |\n| `caret:above` `caret:below` | |\n| `circle` | |\n| corner:downleft | |\n| `scribble` | |\n| `strike` | |\n| `verticalbar` | |\n| `writing` | |\n\n| **Note:** It is not always possible to reliably distinguish some gestures from writing. For example, the `verticalbar` gesture may look exactly like the digit `1` or letter `l` when they are written as a vertical lines. To allow the user to use both gestures and writing, your application may need to consider the position of the writing or gesture: for the writing over existing text, prefer the gesture interpretation; for the writing over empty space, prefer the text interpretation.\n\nEmoji sketches\n\nThe image on the left below shows what the user drew on the screen. The image on\nthe right is the corresponding `Ink` object. It contains the strokes with red\ndots representing the touch points within each stroke.\n\n\nThe `Ink` object contains six strokes.\n\n\n| **Ink** |||\n|----------|-----|-----------------------------|\n| Stroke 1 | `x` | 269, 266, 262, 255, ... |\n| Stroke 1 | `y` | 40, 40, 40, 41, ... |\n| Stroke 1 | `t` | 0, 36, 56, 75, ... |\n| Stroke 2 | `x` | 179, 182, 183, 185, ... |\n| Stroke 2 | `y` | 157, 158, 159, 160, ... |\n| Stroke 2 | `t` | 2475, 2522, 2531, 2541, ... |\n| ... | | |\n\nWhen you send this `Ink` to the emoji recognizer, you get several possible\ntranscriptions, ordered by decreasing confidence:\n\n| **RecognitionResult** ||\n|-------------------------|--------------|\n| RecognitionCandidate #1 | 😂 (U+1f62d) |\n| RecognitionCandidate #2 | 😅 (U+1f605) |\n| RecognitionCandidate #3 | 😹 (U+1f639) |\n| RecognitionCandidate #4 | 😄 (U+1f604) |\n| RecognitionCandidate #5 | 😆 (U+1f606) |"]]
